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                    <h1 class="text-lg md:text-xl font-bold text-gray-800">arXiv 每日论文精选</h1>
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                        <i class="fa fa-calendar-o mr-1"></i>2025-10-15
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                    <span class="text-gray-500 mr-1"><i class="fa fa-file-text-o"></i> 总论文数:</span>
                    <span id="total-papers" class="font-semibold text-primary">157</span>
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                    <span class="text-gray-500 mr-1"><i class="fa fa-star"></i> 精选论文数:</span>
                    <span id="selected-papers" class="font-semibold text-accent">20</span>
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                <span id="display-count" class="font-medium">显示 157 篇论文 (共 157 篇)</span>
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.12801v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>DeepMMSearch-R1：赋能多模态大语言模型在多模态网络搜索中的应用
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            DeepMMSearch-R1: Empowering Multimodal LLMs in Multimodal Web Search
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kartik Narayan, Yang Xu, Tian Cao, Kavya Nerella, Vishal M. Patel, Navid Shiee, ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究多模态LLM在动态网络搜索中的效率问题，核心方法是让模型能够基于输入图像裁剪和迭代文本查询，自主决定何时搜索、搜索什么以及使用哪种搜索工具。</p>
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对搜索领域核心问题，提出多模态LLM动态搜索方法，完美契合直接LLM应用和搜索技术发展重点。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 17:59:58
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12801v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12801v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.IR</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Multimodal Large Language Models (MLLMs) in real-world applications require access to external knowledge sources and must remain responsive to the dynamic and ever-changing real-world information in order to address information-seeking and knowledge-intensive user queries. Existing approaches, such as retrieval augmented generation (RAG) methods, search agents, and search equipped MLLMs, often suffer from rigid pipelines, excessive search calls, and poorly constructed search queries, which result in inefficiencies and suboptimal outcomes. To address these limitations, we present DeepMMSearch-R1, the first multimodal LLM capable of performing on-demand, multi-turn web searches and dynamically crafting queries for both image and text search tools. Specifically, DeepMMSearch-R1 can initiate web searches based on relevant crops of the input image making the image search more effective, and can iteratively adapt text search queries based on retrieved information, thereby enabling self-reflection and self-correction. Our approach relies on a two-stage training pipeline: a cold start supervised finetuning phase followed by an online reinforcement learning optimization. For training, we introduce DeepMMSearchVQA, a novel multimodal VQA dataset created through an automated pipeline intermixed with real-world information from web search tools. This dataset contains diverse, multi-hop queries that integrate textual and visual information, teaching the model when to search, what to search for, which search tool to use and how to reason over the retrieved information. We conduct extensive experiments across a range of knowledge-intensive benchmarks to demonstrate the superiority of our approach. Finally, we analyze the results and provide insights that are valuable for advancing multimodal web-search.
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.12742v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>CTRL-Rec：使用自然语言控制推荐系统
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            CTRL-Rec: Controlling Recommender Systems With Natural Language
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Micah Carroll, Adeline Foote, Kevin Feng, Marcus Williams, Anca Dragan, W. Bradl...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究传统推荐系统缺乏细粒度用户控制的问题，核心方法是利用LLM模拟用户语言请求的偏好判断，训练嵌入模型并将预测集成到推荐信号权重中，实现自然语言实时控制。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接应用LLM技术实现推荐系统的自然语言控制，完美契合直接LLM应用和核心推荐系统改进这两个焦点领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 17:20:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12742v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12742v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.IR</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    When users are dissatisfied with recommendations from a recommender system, they often lack fine-grained controls for changing them. Large language models (LLMs) offer a solution by allowing users to guide their recommendations through natural language requests (e.g., "I want to see respectful posts with a different perspective than mine"). We propose a method, CTRL-Rec, that allows for natural language control of traditional recommender systems in real-time with computational efficiency. Specifically, at training time, we use an LLM to simulate whether users would approve of items based on their language requests, and we train embedding models that approximate such simulated judgments. We then integrate these user-request-based predictions into the standard weighting of signals that traditional recommender systems optimize. At deployment time, we require only a single LLM embedding computation per user request, allowing for real-time control of recommendations. In experiments with the MovieLens dataset, our method consistently allows for fine-grained control across a diversity of requests. In a study with 19 Letterboxd users, we find that CTRL-Rec was positively received by users and significantly enhanced users' sense of control and satisfaction with recommendations compared to traditional controls.
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.12709v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>SAIL-Embedding技术报告：全模态嵌入基础模型
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            SAIL-Embedding Technical Report: Omni-modal Embedding Foundation Model
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Lin Lin, Jiefeng Long, Zhihe Wan, Yuchi Wang, Dingkang Yang, Shuang Yang, Yueyan...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究如何构建支持多模态的统一嵌入模型以解决现实应用中的模态限制和领域差距问题，核心方法是通过多阶段训练策略（包括内容感知渐进训练和推荐增强训练）来提升模型的跨模态能力和推荐场景适应性。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对推荐系统的多模态嵌入模型，提出了内容感知训练和推荐增强训练等核心方法，与用户关注的LLM应用和异构数据处理高度相关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 16:43:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12709v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12709v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">cs.CV</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Multimodal embedding models aim to yield informative unified representations that empower diverse cross-modal tasks. Despite promising developments in the evolution from CLIP-based dual-tower architectures to large vision-language models, prior works still face unavoidable challenges in real-world applications and business scenarios, such as the limited modality support, unstable training mechanisms, and industrial domain gaps. In this work, we introduce SAIL-Embedding, an omni-modal embedding foundation model that addresses these issues through tailored training strategies and architectural design. In the optimization procedure, we propose a multi-stage training scheme to boost the multifaceted effectiveness of representation learning. Specifically, the content-aware progressive training aims to enhance the model's adaptability to diverse downstream tasks and master enriched cross-modal proficiency. The collaboration-aware recommendation enhancement training further adapts multimodal representations for recommendation scenarios by distilling knowledge from sequence-to-item and ID-to-item embeddings while mining user historical interests. Concurrently, we develop the stochastic specialization and dataset-driven pattern matching to strengthen model training flexibility and generalizability. Experimental results show that SAIL-Embedding achieves SOTA performance compared to other methods in different retrieval tasks. In online experiments across various real-world scenarios integrated with our model, we observe a significant increase in Lifetime (LT), which is a crucial indicator for the recommendation experience. For instance, the model delivers the 7-day LT gain of +0.158% and the 14-day LT gain of +0.144% in the Douyin-Selected scenario. For the Douyin feed rank model, the match features produced by SAIL-Embedding yield a +0.08% AUC gain.
                </div>
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<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.12668v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>参数化注入的作用——参数化检索增强生成的系统性研究
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            The Role of Parametric Injection-A Systematic Study of Parametric Retrieval-Augmented Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Minghao Tang, Shiyu Ni, Jingtong Wu, Zengxin Han, Keping Bi
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">研究参数化检索增强生成(PRAG)中参数注入机制的作用，核心发现是参数化文档仅捕获部分语义信息但编码高层文档信息，当与文本文档结合使用时能更有效地利用相关信息并增强模型对噪声输入的鲁棒性。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文系统研究参数化检索增强生成(PRAG)，这是LLM在搜索和推荐领域的重要应用技术，深入分析了参数注入机制对模型-文档交互的影响。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 16:05:01
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12668v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12668v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">cs.CL</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Retrieval-augmented generation (RAG) enhances large language models (LLMs) by retrieving external documents. As an emerging form of RAG, parametric retrieval-augmented generation (PRAG) encodes documents as model parameters (i.e., LoRA modules) and injects these representations into the model during inference, enabling interaction between the LLM and documents at parametric level. Compared with directly placing documents in the input context, PRAG is more efficient and has the potential to offer deeper model-document interaction. Despite its growing attention, the mechanism underlying parametric injection remains poorly understood. In this work, we present a systematic study of PRAG to clarify the role of parametric injection, showing that parameterized documents capture only partial semantic information of documents, and relying on them alone yields inferior performance compared to interaction at text level. However, these parametric representations encode high-level document information that can enhance the model's understanding of documents within the input context. When combined parameterized documents with textual documents, the model can leverage relevant information more effectively and become more robust to noisy inputs, achieving better performance than either source alone. We recommend jointly using parameterized and textual documents and advocate for increasing the information content of parametric representations to advance PRAG.
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.12604v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>SMILE：用于电商搜索点击率预测的语义ID增强冷门物品表示
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
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        <div class="mb-2 text-base text-gray-700">
            SMILE: SeMantic Ids Enhanced CoLd Item Representation for Click-through Rate Prediction in E-commerce SEarch
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qihang Zhao, Zhongbo Sun, Xiaoyang Zheng, Xian Guo, Siyuan Wang, Zihan Liang, Mi...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究电商搜索中冷启动物品的点击率预测问题，核心方法是使用RQ-OPQ编码量化物品内容和协同信息，通过两步对齐策略：RQ编码传递共享协同信号，OPQ编码学习物品差异化信息。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对推荐系统中的冷启动问题，提出基于语义ID的表示增强方法，与推荐系统核心进展和LLM技术应用高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 14:58:50
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12604v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12604v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">cs.AI</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    With the rise of modern search and recommendation platforms, insufficient collaborative information of cold-start items exacerbates the Matthew effect of existing platform items, challenging platform diversity and becoming a longstanding issue. Existing methods align items' side content with collaborative information to transfer collaborative signals from high-popularity items to cold-start items. However, these methods fail to account for the asymmetry between collaboration and content, nor the fine-grained differences among items. To address these issues, we propose SMILE, an item representation enhancement approach based on fused alignment of semantic IDs. Specifically, we use RQ-OPQ encoding to quantize item content and collaborative information, followed by a two-step alignment: RQ encoding transfers shared collaborative signals across items, while OPQ encoding learns differentiated information of items. Comprehensive offline experiments on large-scale industrial datasets demonstrate superiority of SMILE, and rigorous online A/B tests confirm statistically significant improvements: item CTR +1.66%, buyers +1.57%, and order volume +2.17%.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.12461v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>利用语言语义进行协同过滤：TextGCN与TextGCN-MLP的零样本与领域内性能对比
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Leveraging Language Semantics for Collaborative Filtering with TextGCN and TextGCN-MLP: Zero-Shot vs In-Domain Performance
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Andrei Chernov, Haroon Wahab, Oleg Novitskij
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">研究如何利用语言语义改进协同过滤推荐系统；核心方法是直接对基于LLM的物品标题嵌入应用参数无关的图卷积层，替代传统基于ID的嵌入学习，并探索零样本泛化与领域内专业化之间的权衡。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接应用LLM语义嵌入进行推荐系统建模，属于LLM在推荐领域的直接应用，并探索了零样本与领域内性能的权衡问题。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 12:50:11
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12461v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12461v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    In recent years, various approaches have been proposed to leverage large language models (LLMs) for incorporating textual information about items into recommender systems. Existing methods primarily focus on either fine-tuning LLMs to generate recommendations or integrating LLM-based embeddings into downstream models. In this work, we follow the latter direction and propose \textbf{TextGCN}, which applies parameter-free graph convolution layers directly over LLM-based item-title embeddings, instead of learning ID-based embeddings as in traditional methods. By combining language semantics with graph message passing, this architecture achieves state-of-the-art zero-shot performance, significantly outperforming prior approaches. Furthermore, we introduce \textbf{TextGCN-MLP}, which extends TextGCN with a trainable multilayer perceptron trained using a contrastive loss, achieving state-of-the-art in-domain performance on recommendation benchmarks. However, the zero-shot performance of TextGCN-MLP remains lower than that of TextGCN, highlighting the trade-off between in-domain specialization and zero-shot generalization. We release our code on github at \href{https://github.com/ChernovAndrey/TFCE}{github.com/ChernovAndrey/TFCE}.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.12325v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>因果启发的多模态推荐
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Causal Inspired Multi Modal Recommendation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jie Yang, Chenyang Gu, Zixuan Liu
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究多模态推荐系统中的模态混淆和交互偏差问题，核心方法是引入双通道跨模态扩散模块识别隐藏混淆因子，通过后门调整和前门调整构建去混淆因果子图来解决虚假特征-偏好关联。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对推荐系统中的核心偏差问题，提出因果启发的多模态解耦方法，与推荐系统核心进展和LLM在推荐中的应用高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 09:29:07
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12325v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12325v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">cs.AI</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Multimodal recommender systems enhance personalized recommendations in e-commerce and online advertising by integrating visual, textual, and user-item interaction data. However, existing methods often overlook two critical biases: (i) modal confounding, where latent factors (e.g., brand style or product category) simultaneously drive multiple modalities and influence user preference, leading to spurious feature-preference associations; (ii) interaction bias, where genuine user preferences are mixed with noise from exposure effects and accidental clicks. To address these challenges, we propose a Causal-inspired multimodal Recommendation framework. Specifically, we introduce a dual-channel cross-modal diffusion module to identify hidden modal confounders, utilize back-door adjustment with hierarchical matching and vector-quantized codebooks to block confounding paths, and apply front-door adjustment combined with causal topology reconstruction to build a deconfounded causal subgraph. Extensive experiments on three real-world e-commerce datasets demonstrate that our method significantly outperforms state-of-the-art baselines while maintaining strong interpretability.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.12211v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>基于强化学习的推荐系统偏好优化
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Reinforced Preference Optimization for Recommendation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Junfei Tan, Yuxin Chen, An Zhang, Junguang Jiang, Bin Liu, Ziru Xu, Han Zhu, Jia...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究生成式推荐系统中缺乏高质量负样本建模和依赖隐式奖励的问题，核心思想是设计强化偏好优化框架，通过约束束搜索改进采样效率和多样化硬负样本，同时增强基于规则的准确性奖励与辅助排序奖励。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对LLM推荐系统的核心挑战，提出强化学习优化框架，完美契合生成式推荐和LLM应用的研究方向。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 07:04:33
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12211v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12211v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent breakthroughs in large language models (LLMs) have fundamentally shifted recommender systems from discriminative to generative paradigms, where user behavior modeling is achieved by generating target items conditioned on historical interactions. Yet current generative recommenders still suffer from two core limitations: the lack of high-quality negative modeling and the reliance on implicit rewards. Reinforcement learning with verifiable rewards (RLVR) offers a natural solution by enabling on-policy sampling of harder negatives and grounding optimization in explicit reward signals. However, applying RLVR to generative recommenders remains non-trivial. Its unique generation space often leads to invalid or repetitive items that undermine sampling efficiency, and ranking supervision is sparse since most items receive identical zero rewards. To address these challenges, we propose Reinforced Preference Optimization for Recommendation (ReRe), a reinforcement-based paradigm tailored to LLM-based recommenders, an important direction in generative recommendation. ReRe incorporates constrained beam search to improve sampling efficiency and diversify hard negatives, while augmenting rule-based accuracy rewards with auxiliary ranking rewards for finer-grained supervision. Extensive experiments on three real-world datasets demonstrate that ReRe consistently outperforms both traditional and LLM-based recommenders in ranking performance. Further analysis shows that ReRe not only enhances performance across both base and SFT-initialized models but also generalizes robustly across different backbone families and scales. Beyond empirical gains, we systematically investigate the design space of RLVR in recommendation across generation, sampling strategy, reward modeling, and optimization algorithm, offering insights for future research.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.12784v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>SRUM：统一多模态模型的细粒度自奖励机制
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            SRUM: Fine-Grained Self-Rewarding for Unified Multimodal Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Weiyang Jin, Yuwei Niu, Jiaqi Liao, Chengqi Duan, Aoxue Li, Shenghua Gao, Xihui ...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究统一多模态模型中视觉理解与生成能力不匹配的问题，核心思想是设计自奖励后训练框架，让模型的理解模块作为内部评估器，通过全局-局部双奖励系统提供多尺度指导信号来改进生成模块。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的自奖励框架和全局-局部双奖励系统直接适用于多模态推荐系统，其利用理解模块指导生成模块的核心思想与VLM异构数据统一建模高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 17:56:11
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12784v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12784v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.CL</span><span class="category-tag">I.4.0</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recently, remarkable progress has been made in Unified Multimodal Models (UMMs), which integrate vision-language generation and understanding capabilities within a single framework. However, a significant gap exists where a model's strong visual understanding often fails to transfer to its visual generation. A model might correctly understand an image based on user instructions, yet be unable to generate a faithful image from text prompts. This phenomenon directly raises a compelling question: Can a model achieve self-improvement by using its understanding module to reward its generation module? To bridge this gap and achieve self-improvement, we introduce SRUM, a self-rewarding post-training framework that can be directly applied to existing UMMs of various designs. SRUM creates a feedback loop where the model's own understanding module acts as an internal ``evaluator'', providing corrective signals to improve its generation module, without requiring additional human-labeled data. To ensure this feedback is comprehensive, we designed a global-local dual reward system. To tackle the inherent structural complexity of images, this system offers multi-scale guidance: a \textbf{global reward} ensures the correctness of the overall visual semantics and layout, while a \textbf{local reward} refines fine-grained, object-level fidelity. SRUM leads to powerful capabilities and shows strong generalization, boosting performance on T2I-CompBench from 82.18 to \textbf{88.37} and on T2I-ReasonBench from 43.82 to \textbf{46.75}. Overall, our work establishes a powerful new paradigm for enabling a UMMs' understanding module to guide and enhance its own generation via self-rewarding.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.12773v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>Dr.LLM：大语言模型中的动态层路由
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Dr.LLM: Dynamic Layer Routing in LLMs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ahmed Heakl, Martin Gubri, Salman Khan, Sangdoo Yun, Seong Joon Oh
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究LLM处理所有token时计算资源浪费的问题，核心方法是为预训练模型配备轻量级路由器，动态决定跳过、执行或重复Transformer层，在计算预算下保持或提升准确性。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的动态层路由机制直接针对Transformer架构效率优化，属于核心的Transformer技术进步，且可应用于搜索推荐系统的推理加速。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 17:51:26
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12773v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12773v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models (LLMs) process every token through all layers of a transformer stack, causing wasted computation on simple queries and insufficient flexibility for harder ones that need deeper reasoning. Adaptive-depth methods can improve efficiency, but prior approaches rely on costly inference-time search, architectural changes, or large-scale retraining, and in practice often degrade accuracy despite efficiency gains. We introduce Dr.LLM, Dynamic routing of Layers for LLMs, a retrofittable framework that equips pretrained models with lightweight per-layer routers deciding to skip, execute, or repeat a block. Routers are trained with explicit supervision: using Monte Carlo Tree Search (MCTS), we derive high-quality layer configurations that preserve or improve accuracy under a compute budget. Our design, windowed pooling for stable routing, focal loss with class balancing, and bottleneck MLP routers, ensures robustness under class imbalance and long sequences. On ARC (logic) and DART (math), Dr.LLM improves accuracy by up to +3.4%p while saving 5 layers per example on average. Routers generalize to out-of-domain tasks (MMLU, GSM8k, AIME, TruthfulQA, SQuADv2, GPQA, PIQA, AGIEval) with only 0.85% accuracy drop while retaining efficiency, and outperform prior routing methods by up to +7.7%p. Overall, Dr.LLM shows that explicitly supervised routers retrofit frozen LLMs for budget-aware, accuracy-driven inference without altering base weights.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.12643v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>推理模式至关重要：无需人类理性指导的推理学习
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Reasoning Pattern Matters: Learning to Reason without Human Rationales
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chaoxu Pang, Yixuan Cao, Ping Luo
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究如何在不依赖昂贵人工标注的情况下训练LLM的推理能力。核心思想是识别任务中的固定推理模式，让LLM基于这些模式自动生成训练所需的推理轨迹，从而替代大规模人工标注。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对LLM在推荐搜索领域的关键瓶颈——人工标注成本，提出了基于推理模式自动生成标注的方法，属于核心LLM技术进步。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 15:34:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12643v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12643v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities under the widely adopted SFT+RLVR paradigm, which first performs Supervised Fine-Tuning (SFT) on human-annotated reasoning trajectories (rationales) to establish initial reasoning behaviors, then applies Reinforcement Learning with Verifiable Rewards (RLVR) to optimize the model using verifiable signals without golden rationales. However, annotating high-quality rationales for the SFT stage remains prohibitively expensive. This paper investigates when and how rationale annotation costs can be substantially reduced without compromising reasoning performance. We identify a broad class of problems, termed patterned reasoning tasks, where reasoning follows a fixed, procedural strategy consistent across instances. Although instances vary in content such as domain knowledge, factual information, or numeric values, the solution derives from applying a shared reasoning pattern. We argue that the success of SFT+RLVR on such tasks primarily stems from its ability to enable models to internalize these reasoning patterns. Using numerical semantic matching as a representative task, we provide both causal and behavioral evidence showing that reasoning patterns rather than the quantity or quality of rationales are the key determinant of performance. Building on these insights, we propose Pattern-Aware LLMs as Rationale AnnOtators (PARO), a simple yet effective framework that enables LLMs to generate rationales aligned with task-specific reasoning patterns without requiring human rationale annotations. Experiments show that PARO-generated rationales achieve comparable SFT+RLVR performance to human rationales that are 10 times larger. These results suggest that large-scale human rationale annotations can be replaced with LLM-based automatic annotations requiring only limited human supervision over reasoning patterns.
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.12357v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>MoBiLE：在消费级GPU上使用大小专家混合进行高效混合专家推理
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            MoBiLE: Efficient Mixture-of-Experts Inference on Consumer GPU with Mixture of Big Little Experts
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yushu Zhao, Yubin Qin, Yang Wang, Xiaolong Yang, Huiming Han, Shaojun Wei, Yang ...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">研究MoE模型在消费级GPU上的推理效率瓶颈问题，核心方法是设计混合大小专家架构，对不重要token使用半数量专家加速，对重要token保持完整专家保证质量，并配合回退和预取机制优化内存效率。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文针对MoE模型在消费级GPU上的推理效率问题，提出了混合大小专家的创新架构，直接属于Transformer架构效率优化的核心领域。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 10:22:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12357v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12357v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Mixture-of-Experts (MoE) models have recently demonstrated exceptional performance across a diverse range of applications. The principle of sparse activation in MoE models facilitates an offloading strategy, wherein active experts are maintained in GPU HBM, while inactive experts are stored in CPU DRAM. The efficacy of this approach, however, is fundamentally constrained by the limited bandwidth of the CPU-GPU interconnect. To mitigate this bottleneck, existing approaches have employed prefetching to accelerate MoE inference. These methods attempt to predict and prefetch the required experts using specially trained modules. Nevertheless, such techniques are often encumbered by significant training overhead and have shown diminished effectiveness on recent MoE models with fine-grained expert segmentation. In this paper, we propose MoBiLE, a plug-and-play offloading-based MoE inference framework with \textit{mixture of big-little experts}. It reduces the number of experts for unimportant tokens to half for acceleration while maintaining full experts for important tokens to guarantee model quality. Further, a dedicated fallback and prefetching mechanism is designed for switching between little and big experts to improve memory efficiency. We evaluate MoBiLE on four typical modern MoE architectures and challenging generative tasks. Our results show that MoBiLE achieves a speedup of 1.60x to 1.72x compared to the baseline on a consumer GPU system, with negligible degradation in accuracy.
                </div>
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.12178v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>Meta的Llama模型演进与大型语言模型的参数高效微调：综述
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Evolution of meta's llama models and parameter-efficient fine-tuning of large language models: a survey
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Abdulhady Abas Abdullah, Arkaitz Zubiaga, Seyedali Mirjalili, Amir H. Gandomi, F...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文系统梳理了Meta LLaMA系列模型从1代到4代的架构演进（包括多模态和MoE变体），并重点分析了LoRA、QLoRA等参数高效微调方法，这些技术通过仅更新少量参数来适配大模型，为实际应用提供了高效的模型定制方案。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文系统综述了LLaMA模型演进和参数高效微调技术，直接涉及LLM核心进展和高效适配方法，对推荐搜索广告领域的模型部署优化具有重要参考价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 06:12:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12178v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12178v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    This review surveys the rapid evolution of Meta AI's LLaMA (Large Language Model Meta AI) series - from LLaMA 1 through LLaMA 4 and the specialized parameter-efficient fine-tuning (PEFT) methods developed for these models. We first describe the LLaMA family of foundation models (7B-65B to 288B parameters), their architectures (including native multimodal and Mixtureof-Experts variants), and key performance characteristics. We then describe and discuss the concept of PEFT, which adapts large pre-trained models by updating only a small subset of parameters, and review five PEFT methods that have been applied to LLaMA: LoRA (Low-Rank Adaptation), LLaMA-Adapter V1 and V2, LLaMA-Excitor, and QLoRA (Quantized LoRA). We discuss each method's mechanism, parameter savings, and example application to LLaMA (e.g., instruction tuning, multimodal tasks). We provide structured discussion and analysis of model and adapter architectures, parameter counts, and benchmark results (including examples where fine-tuned LLaMA models outperform larger baselines). Finally, we examine real-world use cases where LLaMA-based models and PEFT have been successfully applied (e.g., legal and medical domains), and we discuss ongoing challenges and future research directions (such as scaling to even larger contexts and improving robustness). This survey paper provides a one-stop resource for ML researchers and practitioners interested in LLaMA models and efficient fine-tuning strategies.
                </div>
            </details>
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.12044v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>层次对齐：通过大型语言模型中的功能层专业化进行手术式微调
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Hierarchical Alignment: Surgical Fine-Tuning via Functional Layer Specialization in Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yukun Zhang, Qi Dong
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究LLM对齐过程中忽视模型内部功能分层的问题，核心思想是将DPO优化压力分层施加于处理语法、逻辑和事实性的不同Transformer层，实现手术式精细化微调。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出层级对齐方法，针对Transformer不同功能层进行精细化微调，直接属于Transformer架构效率提升和LLM应用优化的核心领域。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 00:58:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12044v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12044v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Existing alignment techniques for Large Language Models (LLMs), such as Direct Preference Optimization (DPO), typically treat the model as a monolithic entity, applying uniform optimization pressure across all layers. This approach overlooks the functional specialization within the Transformer architecture, where different layers are known to handle distinct tasks from syntax to abstract reasoning. In this paper, we challenge this one-size-fits-all paradigm by introducing Hierarchical Alignment, a novel method that applies targeted DPO to distinct functional blocks of a model's layers: local (syntax), intermediate (logic), and global (factuality). Through a series of controlled experiments on state-of-the-art models like Llama-3.1-8B and Qwen1.5-7B using LoRA for surgical fine-tuning, our results, evaluated by a powerful LLM-as-Judge, demonstrate significant and predictable improvements. Specifically, aligning the local layers (Local-Align) enhances grammatical fluency. More importantly, aligning the global layers (Global-Align) not only improves factual consistency as hypothesized but also proves to be the most effective strategy for enhancing logical coherence, outperforming all baselines. Critically, all hierarchical strategies successfully avoid the "alignment tax" observed in standard DPO, where gains in fluency come at the cost of degraded logical reasoning. These findings establish a more resource-efficient, controllable, and interpretable path for model alignment, highlighting the immense potential of shifting from monolithic optimization to structure-aware surgical fine-tuning to build more advanced and reliable LLMs.
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.12327v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>简单投影变体提升ColBERT性能
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Simple Projection Variants Improve ColBERT Performance
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Benjamin Clavié, Sean Lee, Rikiya Takehi, Aamir Shakir, Makoto P. Kato
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究多向量检索模型中线性投影层的局限性问题，核心思想是用更复杂的FFN网络结构（如深层非线性块、GLU块和残差连接）替代简单线性投影来改善梯度流和表示能力。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文通过改进ColBERT的多向量检索模型投影层设计，直接提升检索系统性能，属于搜索领域的核心算法优化。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 09:34:05
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12327v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12327v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Multi-vector dense retrieval methods like ColBERT systematically use a single-layer linear projection to reduce the dimensionality of individual vectors. In this study, we explore the implications of the MaxSim operator on the gradient flows of the training of multi-vector models and show that such a simple linear projection has inherent, if non-critical, limitations in this setting. We then discuss the theoretical improvements that could result from replacing this single-layer projection with well-studied alternative feedforward linear networks (FFN), such as deeper, non-linear FFN blocks, GLU blocks, and skip-connections, could alleviate these limitations. Through the design and systematic evaluation of alternate projection blocks, we show that better-designed final projections positively impact the downstream performance of ColBERT models. We highlight that many projection variants outperform the original linear projections, with the best-performing variants increasing average performance on a range of retrieval benchmarks across domains by over 2 NDCG@10 points. We then conduct further exploration on the individual parameters of these projections block in order to understand what drives this empirical performance, highlighting the particular importance of upscaled intermediate projections and residual connections. As part of these ablation studies, we show that numerous suboptimal projection variants still outperform the traditional single-layer projection across multiple benchmarks, confirming our hypothesis. Finally, we observe that this effect is consistent across random seeds, further confirming that replacing the linear layer of ColBERT models is a robust, drop-in upgrade.
                </div>
            </details>
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.12054v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>MIARec：面向科学论文推荐的互影响感知异质网络嵌入
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            MIARec: Mutual-influence-aware Heterogeneous Network Embedding for Scientific Paper Recommendation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wenjin Xie, Tao Jia
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究学术论文推荐中忽视学术影响力不对称的问题，核心方法是利用重力模型量化学者间相互学术影响力，并采用多通道聚合机制学习异质学术网络的单关系和跨关系嵌入。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对推荐系统核心问题，提出基于异构图嵌入的学术论文推荐方法，并引入重力模型处理学术影响力不对称性，与推荐系统领域高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 01:47:25
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12054v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12054v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">cs.LG</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    With the rapid expansion of scientific literature, scholars increasingly demand precise and high-quality paper recommendations. Among various recommendation methodologies, graph-based approaches have garnered attention by effectively exploiting the structural characteristics inherent in scholarly networks. However, these methods often overlook the asymmetric academic influence that is prevalent in scholarly networks when learning graph representations. To address this limitation, this study proposes the Mutual-Influence-Aware Recommendation (MIARec) model, which employs a gravity-based approach to measure the mutual academic influence between scholars and incorporates this influence into the feature aggregation process during message propagation in graph representation learning. Additionally, the model utilizes a multi-channel aggregation method to capture both individual embeddings of distinct single relational sub-networks and their interdependent embeddings, thereby enabling a more comprehensive understanding of the heterogeneous scholarly network. Extensive experiments conducted on real-world datasets demonstrate that the MIARec model outperforms baseline models across three primary evaluation metrics, indicating its effectiveness in scientific paper recommendation tasks.
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.12474v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>SMEC：重新思考套娃表示学习在检索嵌入压缩中的应用
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            SMEC: Rethinking Matryoshka Representation Learning for Retrieval Embedding Compression
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Biao Zhang, Lixin Chen, Tong Liu, Bo Zheng
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究高维嵌入导致的检索系统计算和存储瓶颈问题，核心思想是通过顺序嵌套表示学习、自适应维度选择和可选择性跨批次记忆模块，在保持语义信息的同时实现有效的嵌入压缩。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接解决检索系统中高维嵌入的压缩问题，提出了新颖的训练框架和维度选择方法，对搜索和推荐系统的实际部署具有重要价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 13:04:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12474v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12474v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language models (LLMs) generate high-dimensional embeddings that capture rich semantic and syntactic information. However, high-dimensional embeddings exacerbate computational complexity and storage requirements, thereby hindering practical deployment. To address these challenges, we propose a novel training framework named Sequential Matryoshka Embedding Compression (SMEC). This framework introduces the Sequential Matryoshka Representation Learning(SMRL) method to mitigate gradient variance during training, the Adaptive Dimension Selection (ADS) module to reduce information degradation during dimension pruning, and the Selectable Cross-batch Memory (S-XBM) module to enhance unsupervised learning between high- and low-dimensional embeddings. Experiments on image, text, and multimodal datasets demonstrate that SMEC achieves significant dimensionality reduction while maintaining performance. For instance, on the BEIR dataset, our approach improves the performance of compressed LLM2Vec embeddings (256 dimensions) by 1.1 points and 2.7 points compared to the Matryoshka-Adaptor and Search-Adaptor models, respectively.
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.12167v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>面向连续空间推理的推理时扩展方法研究
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Towards Inference-time Scaling for Continuous Space Reasoning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Minghan Wang, Thuy-Trang Vu, Ehsan Shareghi, Gholamreza Haffari
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究如何将离散空间的推理时扩展技术（多样本生成与奖励模型重排序）适配到连续空间推理中。核心发现是连续思维表示缺乏关键归纳偏置，导致正确与错误推理难以区分，需要训练框架显式引入可被推理时利用的归纳偏置。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究推理时扩展技术在连续空间中的应用，直接关联Transformer架构效率和推理优化，对推荐系统中复杂用户行为建模具有重要参考价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 05:53:41
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12167v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12167v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Inference-time scaling through multiple sample generation in combination with Process- or Outcome-Reward Model (PRM or ORM) re-ranking has proven effective for text-based reasoning in large language models. This paper investigates whether such established techniques can be successfully adapted to reasoning in the continuous space, using COCONUT (Hao et al. 2024) continuous space reasoning LM as the backbone. We demonstrate the feasibility of generating diverse reasoning paths through dropout-based sampling. Our Pass@N analysis on the generated samples reveals the potential that could enable a significant gain in performance akin to observed gain in the discrete space. However, we highlight unique challenges faced for materializing this gain in the continuous thought space. In particular, working recipes for data generation and training PRM and ORM models in the discrete space unlocks only marginal improvements in the continuous space. Through probing various aspects including geometric properties and trajectory dynamics we identify the underlying reasons that prevent effective discrimination between correct and incorrect reasoning (essential for the functioning of PRM and ORM). Our findings reveal that current limitations stem from the absence of key inductive biases in continuous thought representations. We argue that the training frameworks for continuous reasoning LMs require not only to optimize for accuracy but also to explicitly incorporate inductive biases that could be utilized during inference-time for discrimination of correct and incorrect thoughts.\footnote{Our code and data will be publicly available.}
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.12121v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>通过目标表示编辑实现大语言模型中的精确属性强度控制
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Precise Attribute Intensity Control in Large Language Models via Targeted Representation Editing
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Rongzhi Zhang, Liqin Ye, Yuzhao Heng, Xiang Chen, Tong Yu, Lingkai Kong, Sudheer...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究LLM输出中精确属性强度控制问题，核心方法是通过训练轻量级值函数预测属性强度，并基于梯度干预隐藏表示来精确导航模型达到特定属性强度目标。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的精确属性强度控制方法通过表示编辑实现细粒度文本生成控制，可直接应用于推荐系统的个性化内容生成和广告文案优化，属于LLM直接应用范畴。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 03:50:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12121v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12121v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
            </div>
            
            
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Precise attribute intensity control--generating Large Language Model (LLM) outputs with specific, user-defined attribute intensities--is crucial for AI systems adaptable to diverse user expectations. Current LLM alignment methods, however, typically provide only directional or open-ended guidance, failing to reliably achieve exact attribute intensities. We address this limitation with three key designs: (1) reformulating precise attribute intensity control as a target-reaching problem, rather than simple maximization; (2) training a lightweight value function via temporal-difference learning to predict final attribute intensity scores from partial generations, thereby steering LLM outputs; and (3) employing gradient-based interventions on hidden representations to navigate the model precisely towards specific attribute intensity targets. Our method enables fine-grained, continuous control over attribute intensities, moving beyond simple directional alignment. Experiments on LLaMA-3.2-3b and Phi-4-mini confirm our method's ability to steer text generation to user-specified attribute intensities with high accuracy. Finally, we demonstrate efficiency enhancements across three downstream tasks: preference data synthesis, Pareto frontier approximation and optimization, and distillation of aligned behaviors for intervention-free inference. Our code is available on https://github.com/Pre-Control/pre-control
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.12051v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>APCE：面向长上下文处理的自适应渐进式上下文扩展
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            APCE: Adaptive Progressive Context Expansion for Long Context Processing
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Baisub Lee, Sanghyun Byun, Mohanad Odema, Jung Guack, Jacob Song, Woo Seong Chun...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">研究长上下文Transformer模型的内存效率和处理性能退化问题；核心方法是基于低维语义相似度匹配，自适应选择最重要的输入块进行处理，从而减少计算开销。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文针对长上下文Transformer的内存效率和性能退化问题，提出通过语义相似度选择重要输入块的方法，直接适用于推荐和搜索系统中的长序列处理。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 01:26:36
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12051v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12051v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Deploying useful Long-Context Transformer Models (LCTMs) requires addressing two key challenges: (1) A growing memory footprint due to quadratic self-attention and linear KV-cache scaling in memory as sequence length increases; (2) the ContextRot phenomena where empirical evidence suggests that transformer architecture's performance degrades with increasing context length. Given the shared dependency on the input, a natural question arises: Can we surgically select the most important input chunks for processing to synergistically (a) reduce the memory footprint, and (b) mitigate the ContextRot effects? In this paper, we answer this question in the affirmative for long-context summarization tasks. We propose APCE as a context-aware solution to select the most important input chunks through low-dimensional semantic similarity matching with the current query. By directly operating on the input, APCE decouples from strict dependency on underlying hardware or CUDA environments, promising a compatible solution scalable to different deployment systems. Our empirical evaluations have demonstrated superior or on-par summarization performance for APCE compared to the full dense baseline using a fraction (50%-70%) of the input sequence resulting in KV-cache and self-attention memory efficiency improvements. We hope our findings inspire further research on context-aware efficiency solutions for LCTMs geared towards other relevant long-context tasks.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.12225v1" target="_blank" rel="noopener noreferrer">
                HoneyBee：面向视觉-语言推理模型的数据配方
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            HoneyBee: Data Recipes for Vision-Language Reasoners
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hritik Bansal, Devandra Singh Sachan, Kai-Wei Chang, Aditya Grover, Gargi Ghosh,...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出为视觉-语言模型设计数据配方的技术，这属于'VLM Analogy for Heterogeneous Data'范畴。通过将异构数据（如上下文特征和用户序列）视为不同模态进行统一建模，该方法可应用于推荐系统中处理多模态用户行为数据，实现更精准的用户意图理解。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 07:23:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12225v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12225v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                    Recent advances in vision-language models (VLMs) have made them highly effective at reasoning tasks. However, the principles underlying the construction of performant VL reasoning training datasets remain poorly understood. In this work, we introduce several data curation approaches and study their impacts on VL reasoning capabilities by carefully controlling training and evaluation setups. We analyze the effects of context (image and question pair) sources, implement targeted data interventions, and explore scaling up images, questions, and chain-of-thought (CoT) solutions. Our findings reveal that (a) context source strategies significantly affect VLM performance, (b) interventions such as auxiliary signals from image captions and the inclusion of text-only reasoning yield substantial gains, and (c) scaling all data dimensions (e.g., unique questions per image and unique CoTs per image-question pair) consistently improves reasoning capability. Motivated by these insights, we introduce HoneyBee, a large-scale, high-quality CoT reasoning dataset with 2.5M examples consisting 350K image-question pairs. VLMs trained with HoneyBee outperform state-of-the-art models across model sizes. For instance, a HoneyBee-trained VLM with 3B parameters outperforms the SOTA model and the base model by 7.8% and 24.8%, respectively, on MathVerse. Furthermore, we propose a test-time scaling strategy that reduces decoding cost by 73% without sacrificing accuracy. Overall, this work presents improved strategies for VL reasoning dataset curation research.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.12184v1" target="_blank" rel="noopener noreferrer">
                CompoDistill：面向多模态大语言模型组合推理的注意力蒸馏
            </a>
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        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
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        <div class="mb-2 text-base text-gray-700">
            CompoDistill: Attention Distillation for Compositional Reasoning in Multimodal LLMs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiwan Kim, Kibum Kim, Sangwoo Seo, Chanyoung Park
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注多模态LLM中的注意力蒸馏技术，属于'Enabling LLM Tech'范畴，可提升模型推理能力。在推荐和搜索系统中，这种组合推理能力可应用于多模态内容理解、用户意图解析和复杂查询处理，提高系统对异构信息的建模精度。注意力蒸馏技术还可优化模型效率，对大规模推荐系统的部署具有实用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 06:27:26
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12184v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12184v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recently, efficient Multimodal Large Language Models (MLLMs) have gained significant attention as a solution to their high computational complexity, making them more practical for real-world applications. In this regard, the knowledge distillation (KD) approach has emerged as a promising alternative, which transfers the rich visual and linguistic knowledge from a larger model (teacher) to a smaller model (student). However, we observe that existing KD methods struggle to effectively distill the teacher MLLM's rich visual perception abilities to the student, a challenge that has been largely overlooked in previous studies. Through a systematic analysis, we identify visual attention misalignment between student and teacher as the main cause of this issue. Based on this insight, we propose CompoDistill, a novel KD framework that explicitly aligns the student's visual attention with that of the teacher to enhance the student's visual perception abilities. Our extensive experiments show that CompoDistill significantly improves performance on compositional reasoning tasks that require visual perception abilities while maintaining strong performance on visual question answering tasks, as done in existing studies. Furthermore, CompoDistill demonstrates effectiveness with a more advanced backbone, highlighting its generalizability.
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            <a href="https://www.alphaxiv.org/abs/2510.12369v1" target="_blank" rel="noopener noreferrer">
                面向任务自适应图表示学习的层次化量化分词框架
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        <div class="mb-2 text-base text-gray-700">
            A Hierarchical Quantized Tokenization Framework for Task-Adaptive Graph Representation Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yang Xiang, Li Fan, Chenke Yin, Chengtao Ji
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出了一种层次化量化分词框架用于图表示学习，这属于Transformer架构的效率优化技术（量化分词），在推荐系统和搜索中有直接应用潜力。图表示学习是推荐系统的核心技术，用于建模用户-物品交互图，而量化技术可以显著提升大规模图神经网络在工业级推荐系统中的推理效率。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 10:36:43
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12369v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12369v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
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                    Recent progress in language and vision foundation models demonstrates the importance of discrete token interfaces that transform complex inputs into compact sequences for large-scale modeling. Extending this paradigm to graphs requires a tokenization scheme that handles non-Euclidean structures and multi-scale dependencies efficiently. Existing approaches to graph tokenization, linearized, continuous, and quantized, remain limited in adaptability and efficiency. In particular, most current quantization-based tokenizers organize hierarchical information in fixed or task-agnostic ways, which may either over-represent or under-utilize structural cues, and lack the ability to dynamically reweight contributions from different levels without retraining the encoder. This work presents a hierarchical quantization framework that introduces a self-weighted mechanism for task-adaptive aggregation across multiple scales. The proposed method maintains a frozen encoder while modulating information flow through a lightweight gating process, enabling parameter-efficient adaptation to diverse downstream tasks. Experiments on benchmark datasets for node classification and link prediction demonstrate consistent improvements over strong baselines under comparable computational budgets.
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            <a href="https://www.alphaxiv.org/abs/2510.12434v1" target="_blank" rel="noopener noreferrer">
                PRoH：基于知识超图的动态规划与推理用于检索增强生成
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            PRoH: Dynamic Planning and Reasoning over Knowledge Hypergraphs for Retrieval-Augmented Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xiangjun Zai, Xingyu Tan, Xiaoyang Wang, Qing Liu, Xiwei Xu, Wenjie Zhang
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">This paper addresses dynamic planning and reasoning over knowledge hypergraphs for RAG systems, which represents enabling LLM technology with clear applications in search and recommendation. The techniques for structured knowledge reasoning and dynamic planning could enhance query understanding, result ranking, and personalized content retrieval in search and recommendation systems.</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 12:13:23
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12434v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12434v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Knowledge Hypergraphs (KHs) have recently emerged as a knowledge representation for retrieval-augmented generation (RAG), offering a paradigm to model multi-entity relations into a structured form. However, existing KH-based RAG methods suffer from three major limitations: static retrieval planning, non-adaptive retrieval execution, and superficial use of KH structure and semantics, which constrain their ability to perform effective multi-hop question answering. To overcome these limitations, we propose PRoH, a dynamic Planning and Reasoning over Knowledge Hypergraphs framework. PRoH incorporates three core innovations: (i) a context-aware planning module that sketches the local KH neighborhood to guide structurally grounded reasoning plan generation; (ii) a structured question decomposition process that organizes subquestions as a dynamically evolving Directed Acyclic Graph (DAG) to enable adaptive, multi-trajectory exploration; and (iii) an Entity-Weighted Overlap (EWO)-guided reasoning path retrieval algorithm that prioritizes semantically coherent hyperedge traversals. Experiments across multiple domains demonstrate that PRoH achieves state-of-the-art performance, surpassing the prior SOTA model HyperGraphRAG by an average of 19.73% in F1 and 8.41% in Generation Evaluation (G-E) score, while maintaining strong robustness in long-range multi-hop reasoning tasks.
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            <a href="https://www.alphaxiv.org/abs/2510.12063v1" target="_blank" rel="noopener noreferrer">
                ThinkPilot：通过自动化思维前缀优化引导推理模型
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            ThinkPilot: Steering Reasoning Models via Automated Think-prefixes Optimization
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sunzhu Li, Zhiyu Lin, Shuling Yang, Jiale Zhao, Wei Chen
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及推理模型的引导优化技术，属于LLM核心能力提升范畴。在推荐系统和搜索领域，这种自动化前缀优化技术可以显著提升复杂推理任务的性能，如多步推荐理由生成、复杂查询理解、以及需要多步推理的个性化推荐场景，直接增强LLM在业务应用中的实用性。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 02:02:19
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12063v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12063v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                    Large Reasoning Models (LRMs) are powerful, but they still suffer from inefficient and off-target reasoning. Currently, training-free methods are limited to either rigid heuristics or descriptive, non-actionable analyses. In this paper, we introduce ThinkPilot, a training-free framework that automatically optimizes LRMs reasoning. It uses an evolutionary process to generate think-prefixes, which are instructions that evolve driven by a taxonomy of reasoning behaviors to guide models toward superior performance. Extensive experiments demonstrate ThinkPilot's broad effectiveness: it significantly improves the accuracy-length trade-off for efficient reasoning, drastically improves safety (for example, cutting the StrongREJECT score of DeepSeek-R1-Distill-Qwen-32B from 27.0% to 0.7), and enhances instruction following. It also synergizes with existing training-based methods. Our analysis reveals that think-prefixes can reliably control LRMs' reasoning behaviors, and that different tasks have strong preferences for specific behavioral distributions. By automatically identifying and eliciting these behaviors, ThinkPilot provides a generalizable framework for aligning LRMs reasoning with task demands. Data and code are available at https://github.com/teqkilla/ThinkPilot
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            <a href="https://www.alphaxiv.org/abs/2510.12376v1" target="_blank" rel="noopener noreferrer">
                深度注意力引导的自适应子采样
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            Deep Attention-guided Adaptive Subsampling
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sharath M Shankaranarayana, Soumava Kumar Roy, Prasad Sudhakar, Chandan Aladahal...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注注意力机制和自适应采样技术，属于'使能Transformer技术'范畴。在推荐系统和搜索中，自适应子采样可用于高效处理长用户序列或大规模候选集，通过注意力引导选择最相关的子集，提升计算效率同时保持模型性能。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 10:50:45
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12376v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12376v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                    Although deep neural networks have provided impressive gains in performance, these improvements often come at the cost of increased computational complexity and expense. In many cases, such as 3D volume or video classification tasks, not all slices or frames are necessary due to inherent redundancies. To address this issue, we propose a novel learnable subsampling framework that can be integrated into any neural network architecture. Subsampling, being a nondifferentiable operation, poses significant challenges for direct adaptation into deep learning models. While some works, have proposed solutions using the Gumbel-max trick to overcome the problem of non-differentiability, they fall short in a crucial aspect: they are only task-adaptive and not inputadaptive. Once the sampling mechanism is learned, it remains static and does not adjust to different inputs, making it unsuitable for real-world applications. To this end, we propose an attention-guided sampling module that adapts to inputs even during inference. This dynamic adaptation results in performance gains and reduces complexity in deep neural network models. We demonstrate the effectiveness of our method on 3D medical imaging datasets from MedMNIST3D as well as two ultrasound video datasets for classification tasks, one of them being a challenging in-house dataset collected under real-world clinical conditions.
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            <a href="https://www.alphaxiv.org/abs/2510.12764v1" target="_blank" rel="noopener noreferrer">
                AnyUp：通用特征上采样
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            AnyUp: Universal Feature Upsampling
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Thomas Wimmer, Prune Truong, Marie-Julie Rakotosaona, Michael Oechsle, Federico ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">特征上采样技术是Transformer架构中的核心效率优化技术，在推荐系统和搜索系统中具有直接应用价值。通过高效的特征上采样，可以提升序列建模、特征交互和表示学习的能力，这对于处理用户行为序列和多模态特征融合至关重要。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 17:45:17
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12764v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12764v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                    We introduce AnyUp, a method for feature upsampling that can be applied to any vision feature at any resolution, without encoder-specific training. Existing learning-based upsamplers for features like DINO or CLIP need to be re-trained for every feature extractor and thus do not generalize to different feature types at inference time. In this work, we propose an inference-time feature-agnostic upsampling architecture to alleviate this limitation and improve upsampling quality. In our experiments, AnyUp sets a new state of the art for upsampled features, generalizes to different feature types, and preserves feature semantics while being efficient and easy to apply to a wide range of downstream tasks.
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            <a href="https://www.alphaxiv.org/abs/2510.12581v1" target="_blank" rel="noopener noreferrer">
                LayerSync：自对齐中间层
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            LayerSync: Self-aligning Intermediate Layers
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yasaman Haghighi, Bastien van Delft, Mariam Hassan, Alexandre Alahi
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及中间层对齐技术，属于Transformer架构效率优化范畴，与'Enabling Transformer Tech'相关。在推荐系统或搜索中，中间层对齐可提升模型训练稳定性、加速收敛，并可能改善多任务学习效果，对大规模部署具有实用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 14:39:14
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12581v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12581v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We propose LayerSync, a domain-agnostic approach for improving the generation quality and the training efficiency of diffusion models. Prior studies have highlighted the connection between the quality of generation and the representations learned by diffusion models, showing that external guidance on model intermediate representations accelerates training. We reconceptualize this paradigm by regularizing diffusion models with their own intermediate representations. Building on the observation that representation quality varies across diffusion model layers, we show that the most semantically rich representations can act as an intrinsic guidance for weaker ones, reducing the need for external supervision. Our approach, LayerSync, is a self-sufficient, plug-and-play regularizer term with no overhead on diffusion model training and generalizes beyond the visual domain to other modalities. LayerSync requires no pretrained models nor additional data. We extensively evaluate the method on image generation and demonstrate its applicability to other domains such as audio, video, and motion generation. We show that it consistently improves the generation quality and the training efficiency. For example, we speed up the training of flow-based transformer by over 8.75x on ImageNet dataset and improved the generation quality by 23.6%. The code is available at https://github.com/vita-epfl/LayerSync.
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            <a href="https://www.alphaxiv.org/abs/2510.12699v1" target="_blank" rel="noopener noreferrer">
                生成空间大小：理解和校准大语言模型生成结果的开放性
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Generation Space Size: Understanding and Calibrating Open-Endedness of LLM Generations
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sunny Yu, Ahmad Jabbar, Robert Hawkins, Dan Jurafsky, Myra Cheng
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM生成结果的开放性和校准问题，这属于纯粹的LLM评估和可控生成范畴。虽然LLM校准在理论上可能间接影响推荐/搜索系统中的生成质量，但论文没有明确展示与RecSys/Search/Ads领域的直接应用联系，且更偏向于NLP评估基准问题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 16:31:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12699v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12699v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Different open-ended generation tasks require different degrees of output diversity. However, current LLMs are often miscalibrated. They collapse to overly homogeneous outputs for creative tasks and hallucinate diverse but incorrect responses for factual tasks. We argue that these two failure modes are unified by, and can both be addressed by, the notion of effective generation space size (GSS) -- the set of semantically distinct outputs a model considers for a prompt. We present GSSBench, a task suite of prompt pairs with ground-truth GSS relationships to assess different metrics and understand where models diverge from desired behavior. We find that hallucination detection metrics, particularly EigenScore, consistently outperform standard diversity and uncertainty quantification metrics, while using only model internals, providing interpretable insights into a model's internal task representations. We demonstrate three applications of GSS: (1) detecting prompt ambiguity and predicting clarification questions for better grounding, (2) interpreting overthinking and underthinking in reasoning models, and (3) steering models to expand their generation space to yield high-quality and diverse outputs.
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            <a href="https://www.alphaxiv.org/abs/2510.12680v1" target="_blank" rel="noopener noreferrer">
                揭秘混合思维：大型语言模型能否真正在思考与非思考模式间切换？
            </a>
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Demystifying Hybrid Thinking: Can LLMs Truly Switch Between Think and No-Think?
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shouren Wang, Wang Yang, Xianxuan Long, Qifan Wang, Vipin Chaudhary, Xiaotian Ha...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要探讨LLM的思维模式切换能力，属于LLM基础能力研究。虽然涉及LLM内部工作机制，但缺乏明确的推荐系统、搜索或广告应用场景的直接关联。对于'赋能LLM技术'类别，需要说明其在RecSys/Search/Ads中的潜在应用，但该标题未提供足够信息来判断这种应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 16:19:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12680v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12680v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Hybrid thinking enables LLMs to switch between reasoning and direct answering, offering a balance between efficiency and reasoning capability. Yet our experiments reveal that current hybrid thinking LLMs only achieve partial mode separation: reasoning behaviors often leak into the no-think mode. To understand and mitigate this, we analyze the factors influencing controllability and identify four that matter most: (1) larger data scale, (2) using think and no-think answers from different questions rather than the same question, (3) a moderate increase in no-think data number, and (4) a two-phase strategy that first trains reasoning ability and then applies hybrid think training. Building on these findings, we propose a practical recipe that, compared to standard training, can maintain accuracy in both modes while significantly reducing no-think output length (from $1085$ to $585$ on MATH500) and occurrences of reasoning-supportive tokens such as ``\texttt{wait}'' (from $5917$ to $522$ on MATH500). Our findings highlight the limitations of current hybrid thinking and offer directions for strengthening its controllability.
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            <a href="https://www.alphaxiv.org/abs/2510.12603v1" target="_blank" rel="noopener noreferrer">
                暗处推理：潜在空间中的交错视觉-文本推理
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Reasoning in the Dark: Interleaved Vision-Text Reasoning in Latent Space
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chao Chen, Zhixin Ma, Yongqi Li, Yupeng Hu, Yinwei Wei, Wenjie Li, Liqiang Nie
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉-文本模态的联合推理，属于多模态学习范畴。虽然视觉-语言模型（VLM）的类比在异构数据处理方面有潜在相关性，但该论文更侧重于纯粹的视觉-文本推理机制，而非明确的推荐/搜索/广告应用。对于推荐系统，这种潜在空间推理可能应用于处理商品图像和文本描述的异构特征，但连接不够直接。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 14:58:25
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12603v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12603v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Multimodal reasoning aims to enhance the capabilities of MLLMs by incorporating intermediate reasoning steps before reaching the final answer. It has evolved from text-only reasoning to the integration of visual information, enabling the thought process to be conveyed through both images and text. Despite its effectiveness, current multimodal reasoning methods depend on explicit reasoning steps that require labor-intensive vision-text annotations and inherently introduce significant inference latency. To address these issues, we introduce multimodal latent reasoning with the advantages of multimodal representation, reduced annotation, and inference efficiency. To facilicate it, we propose Interleaved Vision-Text Latent Reasoning (IVT-LR), which injects both visual and textual information in the reasoning process within the latent space. Specifically, IVT-LR represents each reasoning step by combining two implicit parts: latent text (the hidden states from the previous step) and latent vision (a set of selected image embeddings). We further introduce a progressive multi-stage training strategy to enable MLLMs to perform the above multimodal latent reasoning steps. Experiments on M3CoT and ScienceQA demonstrate that our IVT-LR method achieves an average performance increase of 5.45% in accuracy, while simultaneously achieving a speed increase of over 5 times compared to existing approaches. Code available at https://github.com/FYYDCC/IVT-LR.
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            <a href="https://www.alphaxiv.org/abs/2510.12587v1" target="_blank" rel="noopener noreferrer">
                教导语言模型忠实地表达其不确定性
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        <div class="mb-2 text-base text-gray-700">
            Teaching Language Models to Faithfully Express their Uncertainty
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Bryan Eikema, Evgenia Ilia, José G. C. de Souza, Chrysoula Zerva, Wilker Aziz
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注语言模型的不确定性表达，这属于LLM评估和可靠性范畴，而非核心推荐系统、搜索或广告的技术进展。虽然不确定性建模在理论上可能应用于推荐系统的置信度估计，但论文标题未明确指向这些领域的实际应用，更偏向纯粹的NLP可靠性研究。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 14:42:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12587v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12587v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language models (LLMs) often miscommunicate their uncertainty: repeated queries can produce divergent answers, yet generated responses are typically unhedged or hedged in ways that do not reflect this variability. This conveys unfaithful information about the uncertain state of the LLMs' knowledge, creating a faithfulness gap that affects even strong LLMs. We introduce Faithful Uncertainty Tuning (FUT): a fine-tuning approach that teaches instruction-tuned LLMs to express uncertainty faithfully without altering their underlying answer distribution. We construct training data by augmenting model samples with uncertainty hedges (i.e. verbal cues such as 'possibly' or 'likely') aligned with sample consistency, requiring no supervision beyond the model and a set of prompts. We evaluate FUT on open-domain question answering (QA) across multiple models and datasets. Our results show that FUT substantially reduces the faithfulness gap, while preserving QA accuracy and introducing minimal semantic distribution shift. Further analyses demonstrate robustness across decoding strategies, choice of hedgers, and other forms of uncertainty expression (i.e. numerical). These findings establish FUT as a simple and effective way to teach LLMs to communicate uncertainty faithfully.
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            <a href="https://www.alphaxiv.org/abs/2510.12287v1" target="_blank" rel="noopener noreferrer">
                视觉语言模型通过视觉投影器中的语义纠缠将Logo映射到文本
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Vision Language Models Map Logos to Text via Semantic Entanglement in the Visual Projector
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sifan Li, Hongkai Chen, Yujun Cai, Qingwen Ye, Liyang Chen, Junsong Yuan, Yiwei ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究视觉语言模型中Logo与文本的映射机制，属于视觉-语言多模态研究。虽然VLM技术对处理异构数据有启发价值，但Logo识别在搜索/推荐/广告中的直接应用有限，且论文更偏重视觉模态的机制分析而非通用异构数据处理框架。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 08:42:58
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12287v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12287v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Vision Language Models (VLMs) have achieved impressive progress in multimodal reasoning; yet, they remain vulnerable to hallucinations, where outputs are not grounded in visual evidence. In this paper, we investigate a previously overlooked setting: logo hallucination, where models generate brand names or textual content despite logos containing no visible words. Using curated splits of pure symbols, hybrids, and text-bearing logos, as well as the challenging Hard-60 subset, we systematically measure hallucination across leading VLMs. We further probe robustness through nine structured perturbations and show that hallucinations persist even under strong distortions, with occlusion exposing the sharpest weaknesses. Embedding-level analysis with open-weight LLaVA demonstrates that hallucination is tied to a small subset of projector dimensions, and targeted ablation substantially reduces errors while preserving OCR accuracy. Together, these findings reveal that VLMs often rely on symbolic priors rather than genuine glyph perception, particularly for iconic circular logos, and that projector subspaces play a decisive role in this failure mode. Our work contributes both a novel diagnostic lens and actionable mitigation insights, highlighting projector disentanglement and OCR-guided decoding as promising directions for building more trustworthy multimodal systems.
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            <a href="https://www.alphaxiv.org/abs/2510.12285v1" target="_blank" rel="noopener noreferrer">
                采用全词掩码的中文ModernBERT模型
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            <i class="fa fa-star mr-1"></i>3/10
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            Chinese ModernBERT with Whole-Word Masking
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zeyu Zhao, Ningtao Wang, Xing Fu, Yu Cheng
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于中文BERT模型的改进（ModernBERT架构和全词掩码技术），属于基础语言模型技术。虽然语言模型是搜索和推荐系统的核心组件，但该工作主要针对中文NLP优化，未明确涉及推荐、搜索或广告领域的特定应用或架构创新，因此相关性有限。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 08:41:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12285v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12285v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Encoder-only Transformers have advanced along three axes -- architecture, data, and systems -- yielding Pareto gains in accuracy, speed, and memory efficiency. Yet these improvements have not fully transferred to Chinese, where tokenization and morphology differ markedly from English. We introduce Chinese ModernBERT, a from-scratch Chinese encoder that couples: (i) a hardware-aware 32k BPE vocabulary tailored to frequent Chinese affixes/compounds, lowering the embedding budget; (ii) whole-word masking (WWM) with a dynamic masking curriculum (30% -> 15%) to align task difficulty with training progress; (iii) a two-stage pre-training pipeline that extends the native context from 1,024 to 8,192 tokens using RoPE and alternating local/global attention; and (iv) a damped-cosine learning-rate schedule for stable long-horizon optimization. We pre-train on ~1.2T Chinese tokens from CCI3-HQ, CCI4 (Chinese), and Cosmopedia-Chinese. On CLUE, Chinese ModernBERT is competitive with strong Chinese encoders under a unified fine-tuning protocol. Under bf16 it achieves high long-sequence throughput while maintaining strong short-sequence speed, reflecting benefits from budget allocation and attention design. To probe retrieval-oriented quality, we add a small amount of open contrastive data: fine-tuning on SimCLUE (~3M pairs) improves further when adding T2Ranking (~2M), reaching 0.505 (Pearson) / 0.537 (Spearman) on the SimCLUE test set. Under this open-data setting, Chinese ModernBERT surpasses Qwen-0.6B-embedding on SimCLUE, suggesting a clear scaling path for STS with additional curated pairs. We will release tokenizer and weights to facilitate reproducible research.
                </div>
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.12251v1" target="_blank" rel="noopener noreferrer">
                DSAS：多文档问答中注意力优化的通用即插即用框架
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            DSAS: A Universal Plug-and-Play Framework for Attention Optimization in Multi-Document Question Answering
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiakai Li, Rongzheng Wang, Yizhuo Ma, Shuang Liang, Guangchun Luo, Ke Qin
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于注意力优化技术，这属于Transformer架构效率改进范畴，对搜索系统中的多文档检索和问答有潜在应用价值。然而，论文明确限定在多文档问答场景，与推荐系统或广告的直接关联较弱，通用性有限。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 08:01:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12251v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12251v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    While large language models (LLMs) show considerable promise across various fields, they have notable limitations in handling multi-document question answering (Multi-doc QA) tasks. The first challenge is long-range dependency modeling, where LLMs struggle to focus on key information in long texts, which weakens important semantic connections. Second, most LLMs suffer from the ''lost-in-the-middle'' issue, where they have difficulty processing information in the middle of long inputs. Current solutions either truncate global dependencies or demand costly finetuning, ultimately lacking a universal and simple solution for these challenges. To resolve these limitations, we propose Dual-Stage Adaptive Sharpening (DSAS) containing two modules. (i) The Contextual Gate Weighting (CGW) module alleviates ''lost-in-the-middle'' by assessing paragraph relevance through layer-wise attention tracking and position-aware weighting. (ii) The Reciprocal Attention Suppression (RAS) module enhances focus on critical paragraphs by suppressing information exchange between key and irrelevant texts, thus mitigating the limitations in long-range dependency modeling. Notably, DSAS functions as a plug-and-play solution requiring no architectural modifications or extra training parameters. Extensive experiments on four benchmarks demonstrate DSAS's efficacy across mainstream LLMs (Llama, Qwen, Mistral, and Deepseek), with an average F1-score improvement of 4.2% in Multi-doc QA tasks on Llama-3.1-8B-Instruct and Qwen2.5-14B-Instruct. Ablation studies confirm the essential contributions of both the CGW and RAS modules. In addition, detailed discussions in the Appendix further validate the robustness and scalability of DSAS.
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            <a href="https://www.alphaxiv.org/abs/2510.12798v1" target="_blank" rel="noopener noreferrer">
                通过下一位置预测检测任意目标
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Detect Anything via Next Point Prediction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qing Jiang, Junan Huo, Xingyu Chen, Yuda Xiong, Zhaoyang Zeng, Yihao Chen, Tianh...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出了一种通用的检测方法，可能属于计算机视觉领域的基础技术。虽然检测技术在搜索和推荐系统中可用于内容理解（如图像/视频分析），但论文标题未明确表明与推荐系统、搜索或广告的直接关联，且未提及Transformer架构或LLM技术。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 17:59:54
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12798v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12798v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Object detection has long been dominated by traditional coordinate regression-based models, such as YOLO, DETR, and Grounding DINO. Although recent efforts have attempted to leverage MLLMs to tackle this task, they face challenges like low recall rate, duplicate predictions, coordinate misalignment, etc. In this work, we bridge this gap and propose Rex-Omni, a 3B-scale MLLM that achieves state-of-the-art object perception performance. On benchmarks like COCO and LVIS, Rex-Omni attains performance comparable to or exceeding regression-based models (e.g., DINO, Grounding DINO) in a zero-shot setting. This is enabled by three key designs: 1) Task Formulation: we use special tokens to represent quantized coordinates from 0 to 999, reducing the model's learning difficulty and improving token efficiency for coordinate prediction; 2) Data Engines: we construct multiple data engines to generate high-quality grounding, referring, and pointing data, providing semantically rich supervision for training; \3) Training Pipelines: we employ a two-stage training process, combining supervised fine-tuning on 22 million data with GRPO-based reinforcement post-training. This RL post-training leverages geometry-aware rewards to effectively bridge the discrete-to-continuous coordinate prediction gap, improve box accuracy, and mitigate undesirable behaviors like duplicate predictions that stem from the teacher-guided nature of the initial SFT stage. Beyond conventional detection, Rex-Omni's inherent language understanding enables versatile capabilities such as object referring, pointing, visual prompting, GUI grounding, spatial referring, OCR and key-pointing, all systematically evaluated on dedicated benchmarks. We believe that Rex-Omni paves the way for more versatile and language-aware visual perception systems.
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            <a href="https://www.alphaxiv.org/abs/2510.12691v1" target="_blank" rel="noopener noreferrer">
                DiffEM：通过期望最大化利用扩散模型从损坏数据中学习
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            DiffEM: Learning from Corrupted Data with Diffusion Models via Expectation Maximization
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Danial Hosseintabar, Fan Chen, Giannis Daras, Antonio Torralba, Constantinos Das...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注扩散模型在损坏数据上的学习问题，属于生成模型的技术改进。虽然扩散模型是LLM相关技术，但论文聚焦于数据损坏场景下的学习算法，与推荐系统、搜索或广告的核心应用关联较弱。期望最大化算法可能对处理推荐系统中的噪声数据有潜在价值，但论文标题未明确指向这些领域的具体应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 16:25:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12691v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12691v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span></div>
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                    Diffusion models have emerged as powerful generative priors for high-dimensional inverse problems, yet learning them when only corrupted or noisy observations are available remains challenging. In this work, we propose a new method for training diffusion models with Expectation-Maximization (EM) from corrupted data. Our proposed method, DiffEM, utilizes conditional diffusion models to reconstruct clean data from observations in the E-step, and then uses the reconstructed data to refine the conditional diffusion model in the M-step. Theoretically, we provide monotonic convergence guarantees for the DiffEM iteration, assuming appropriate statistical conditions. We demonstrate the effectiveness of our approach through experiments on various image reconstruction tasks.
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            <a href="https://www.alphaxiv.org/abs/2510.12425v1" target="_blank" rel="noopener noreferrer">
                基于单调包含的张量补全：广义低秩先验与深度去噪器的结合
            </a>
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Tensor Completion via Monotone Inclusion: Generalized Low-Rank Priors Meet Deep Denoisers
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Peng Chen, Deliang Wei, Jiale Yao, Fang Li
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注张量补全的数学优化方法，属于通用的矩阵/张量补全技术，而非专门针对推荐系统、搜索或广告领域的核心进展。虽然张量补全在推荐系统中可用于处理稀疏交互数据，但论文聚焦于单调包含优化和深度去噪器的理论结合，缺乏明确的RecSys/Search/Ads应用场景说明，因此相关性有限。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 12:01:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12425v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12425v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">math.OC</span><span class="category-tag">cs.CV</span><span class="category-tag">65K10</span><span class="category-tag">68T07</span><span class="category-tag">94A08</span></div>
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                    Missing entries in multi dimensional data pose significant challenges for downstream analysis across diverse real world applications. These data are naturally modeled as tensors, and recent completion methods integrating global low rank priors with plug and play denoisers have demonstrated strong empirical performance. However, these approaches often rely on empirical convergence alone or unrealistic assumptions, such as deep denoisers acting as proximal operators of implicit regularizers, which generally does not hold. To address these limitations, we propose a novel tensor completion framework grounded in the monotone inclusion paradigm, which unifies generalized low rank priors with deep pseudo contractive denoisers and extends beyond traditional convex optimization. Building on the Davis Yin splitting scheme, we develop the GTCTV DPC algorithm and rigorously establish its global convergence. Extensive experiments demonstrate that GTCTV DPC consistently outperforms existing methods in both quantitative metrics and visual quality, particularly at low sampling rates.
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            <a href="https://www.alphaxiv.org/abs/2510.12283v1" target="_blank" rel="noopener noreferrer">
                基于动态知识蒸馏与软对齐的部分相关视频检索双学习
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            Dual Learning with Dynamic Knowledge Distillation and Soft Alignment for Partially Relevant Video Retrieval
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jianfeng Dong, Lei Huang, Daizong Liu, Xianke Chen, Xun Yang, Changting Lin, Xun...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视频检索中的部分相关性匹配问题，虽然涉及知识蒸馏和检索技术，但其核心应用场景是视频内容检索而非推荐系统、搜索或广告领域。动态知识蒸馏技术可能对模型效率有一定启发，但缺乏明确的RecSys/Search/Ads应用场景的直接关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 08:38:20
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12283v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12283v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Almost all previous text-to-video retrieval works ideally assume that videos are pre-trimmed with short durations containing solely text-related content. However, in practice, videos are typically untrimmed in long durations with much more complicated background content. Therefore, in this paper, we focus on the more practical yet challenging task of Partially Relevant Video Retrieval (PRVR), which aims to retrieve partially relevant untrimmed videos with the given query. To tackle this task, we propose a novel framework that distills generalization knowledge from a powerful large-scale vision-language pre-trained model and transfers it to a lightweight, task-specific PRVR network. Specifically, we introduce a Dual Learning framework with Dynamic Knowledge Distillation (DL-DKD++), where a large teacher model provides supervision to a compact dual-branch student network. The student model comprises two branches: an inheritance branch that absorbs transferable knowledge from the teacher, and an exploration branch that learns task-specific information from the PRVR dataset to address domain gaps. To further enhance learning, we incorporate a dynamic soft-target construction mechanism. By replacing rigid hard-target supervision with adaptive soft targets that evolve during training, our method enables the model to better capture the fine-grained, partial relevance between videos and queries. Experiment results demonstrate that our proposed model achieves state-of-the-art performance on TVR, ActivityNet, and Charades-STA datasets for PRVR. The code is available at https://github.com/HuiGuanLab/DL-DKD.
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            <a href="https://www.alphaxiv.org/abs/2510.12299v1" target="_blank" rel="noopener noreferrer">
                基于多模态大语言模型的视频问答中视频表征的实证研究
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            <i class="fa fa-star mr-1"></i>2/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            An Empirical Study for Representations of Videos in Video Question Answering via MLLMs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhi Li, Yanan Wang, Hao Niu, Julio Vizcarra, Masato Taya
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视频问答任务，属于纯粹的视觉-语言多模态研究领域。虽然涉及多模态大语言模型技术，但其应用场景（视频问答）与推荐系统、搜索或广告的核心业务需求没有直接关联，且缺乏明确的跨模态建模技术迁移路径。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 09:02:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12299v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12299v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">I.2.10</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Multimodal large language models have recently achieved remarkable progress in video question answering (VideoQA) by jointly processing visual, textual, and audio information. However, it remains unclear which video representations are most effective for MLLMs, and how different modalities balance task accuracy against computational efficiency. In this work, we present a comprehensive empirical study of video representation methods for VideoQA with MLLMs. We systematically evaluate single modality inputs question only, subtitles, visual frames, and audio signals as well as multimodal combinations, on two widely used benchmarks: VideoMME and LongVideoBench. Our results show that visual frames substantially enhance accuracy but impose heavy costs in GPU memory and inference latency, while subtitles provide a lightweight yet effective alternative, particularly for long videos. These findings highlight clear trade-offs between effectiveness and efficiency and provide practical insights for designing resource-aware MLLM-based VideoQA systems.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.12766v1" target="_blank" rel="noopener noreferrer">
                语言模型建模语言
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Language Models Model Language
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Łukasz Borchmann
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该标题讨论语言模型的基本语言建模能力，属于纯粹的LLM理论研究。虽然涉及核心LLM技术，但缺乏明确的推荐系统、搜索或广告应用潜力，更偏向基础NLP研究而非实际应用导向。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 17:45:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12766v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12766v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Linguistic commentary on LLMs, heavily influenced by the theoretical frameworks of de Saussure and Chomsky, is often speculative and unproductive. Critics challenge whether LLMs can legitimately model language, citing the need for "deep structure" or "grounding" to achieve an idealized linguistic "competence." We argue for a radical shift in perspective towards the empiricist principles of Witold Ma\'nczak, a prominent general and historical linguist. He defines language not as a "system of signs" or a "computational system of the brain" but as the totality of all that is said and written. Above all, he identifies frequency of use of particular language elements as language's primary governing principle. Using his framework, we challenge prior critiques of LLMs and provide a constructive guide for designing, evaluating, and interpreting language models.
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            <a href="https://www.alphaxiv.org/abs/2510.12740v1" target="_blank" rel="noopener noreferrer">
                嘿，稍等一下：论语言模型中的议题敏感性
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Hey, wait a minute: on at-issue sensitivity in Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sanghee J. Kim, Kanishka Misra
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文主要研究语言模型中的议题敏感性，这属于纯NLP领域的语言学分析，与推荐系统、搜索或广告的核心技术没有直接关联。虽然语言理解能力可能间接影响这些领域，但论文焦点过于偏向基础NLP理论，缺乏明确的实际应用场景或技术改进方案。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 17:17:20
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12740v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12740v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    Evaluating the naturalness of dialogue in language models (LMs) is not trivial: notions of 'naturalness' vary, and scalable quantitative metrics remain limited. This study leverages the linguistic notion of 'at-issueness' to assess dialogue naturalness and introduces a new method: Divide, Generate, Recombine, and Compare (DGRC). DGRC (i) divides a dialogue as a prompt, (ii) generates continuations for subparts using LMs, (iii) recombines the dialogue and continuations, and (iv) compares the likelihoods of the recombined sequences. This approach mitigates bias in linguistic analyses of LMs and enables systematic testing of discourse-sensitive behavior. Applying DGRC, we find that LMs prefer to continue dialogue on at-issue content, with this effect enhanced in instruct-tuned models. They also reduce their at-issue preference when relevant cues (e.g., "Hey, wait a minute") are present. Although instruct-tuning does not further amplify this modulation, the pattern reflects a hallmark of successful dialogue dynamics.
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            <a href="https://www.alphaxiv.org/abs/2510.12722v1" target="_blank" rel="noopener noreferrer">
                哪些词序促进语言模型中的长度泛化？基于GCG的人工语言研究
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        <div class="mb-2 text-base text-gray-700">
            Which Word Orders Facilitate Length Generalization in LMs? An Investigation with GCG-Based Artificial Languages
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Nadine El-Naggar, Tatsuki Kuribayashi, Ted Briscoe
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究语言模型的长度泛化能力和词序影响，属于基础NLP机制研究。虽然涉及语言模型架构，但缺乏明确的推荐系统、搜索或广告应用场景，且研究重点偏向纯语言理解而非实际工业应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 17:00:19
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12722v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12722v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Whether language models (LMs) have inductive biases that favor typologically frequent grammatical properties over rare, implausible ones has been investigated, typically using artificial languages (ALs) (White and Cotterell, 2021; Kuribayashi et al., 2024). In this paper, we extend these works from two perspectives. First, we extend their context-free AL formalization by adopting Generalized Categorial Grammar (GCG) (Wood, 2014), which allows ALs to cover attested but previously overlooked constructions, such as unbounded dependency and mildly context-sensitive structures. Second, our evaluation focuses more on the generalization ability of LMs to process unseen longer test sentences. Thus, our ALs better capture features of natural languages and our experimental paradigm leads to clearer conclusions -- typologically plausible word orders tend to be easier for LMs to productively generalize.
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            <a href="https://www.alphaxiv.org/abs/2510.12720v1" target="_blank" rel="noopener noreferrer">
                全能描述器：面向全方位细节感知的数据流水线、模型与基准
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Omni-Captioner: Data Pipeline, Models, and Benchmark for Omni Detailed Perception
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ziyang Ma, Ruiyang Xu, Zhenghao Xing, Yunfei Chu, Yuxuan Wang, Jinzheng He, Jin ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉细节感知和图像描述任务，属于视觉-语言模型(VLM)范畴，但与推荐系统、搜索或广告的直接关联较弱。虽然VLM技术可能为处理异构数据提供灵感，但论文标题未明确显示其在RecSys/Search/Ads领域的应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 17:00:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12720v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12720v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.CV</span><span class="category-tag">cs.MM</span><span class="category-tag">cs.SD</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Fine-grained perception of multimodal information is critical for advancing human-AI interaction. With recent progress in audio-visual technologies, Omni Language Models (OLMs), capable of processing audio and video signals in parallel, have emerged as a promising paradigm for achieving richer understanding and reasoning. However, their capacity to capture and describe fine-grained details remains limited explored. In this work, we present a systematic and comprehensive investigation of omni detailed perception from the perspectives of the data pipeline, models, and benchmark. We first identify an inherent "co-growth" between detail and hallucination in current OLMs. To address this, we propose Omni-Detective, an agentic data generation pipeline integrating tool-calling, to autonomously produce highly detailed yet minimally hallucinatory multimodal data. Based on the data generated with Omni-Detective, we train two captioning models: Audio-Captioner for audio-only detailed perception, and Omni-Captioner for audio-visual detailed perception. Under the cascade evaluation protocol, Audio-Captioner achieves the best performance on MMAU and MMAR among all open-source models, surpassing Gemini 2.5 Flash and delivering performance comparable to Gemini 2.5 Pro. On existing detailed captioning benchmarks, Omni-Captioner sets a new state-of-the-art on VDC and achieves the best trade-off between detail and hallucination on the video-SALMONN 2 testset. Given the absence of a dedicated benchmark for omni detailed perception, we design Omni-Cloze, a novel cloze-style evaluation for detailed audio, visual, and audio-visual captioning that ensures stable, efficient, and reliable assessment. Experimental results and analysis demonstrate the effectiveness of Omni-Detective in generating high-quality detailed captions, as well as the superiority of Omni-Cloze in evaluating such detailed captions.
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            <a href="https://www.alphaxiv.org/abs/2510.12692v1" target="_blank" rel="noopener noreferrer">
                谁是更好的匹配者？高风险创业竞赛中人类与算法评委分配的比较
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            Who is a Better Matchmaker? Human vs. Algorithmic Judge Assignment in a High-Stakes Startup Competition
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sarina Xi, Orelia Pi, Miaomiao Zhang, Becca Xiong, Jacqueline Ng Lane, Nihar B. ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究人类与算法在评委分配中的比较，这属于匹配算法在特定竞赛场景的应用。虽然涉及算法匹配，但缺乏与推荐系统、搜索或广告的直接关联，且不涉及LLM、Transformer架构或异构数据建模等核心技术。其应用场景过于特定，通用性有限。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 16:25:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12692v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12692v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.HC</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.CY</span><span class="category-tag">cs.LG</span></div>
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                    There is growing interest in applying artificial intelligence (AI) to automate and support complex decision-making tasks. However, it remains unclear how algorithms compare to human judgment in contexts requiring semantic understanding and domain expertise. We examine this in the context of the judge assignment problem, matching submissions to suitably qualified judges. Specifically, we tackled this problem at the Harvard President's Innovation Challenge, the university's premier venture competition awarding over \$500,000 to student and alumni startups. This represents a real-world environment where high-quality judge assignment is essential. We developed an AI-based judge-assignment algorithm, Hybrid Lexical-Semantic Similarity Ensemble (HLSE), and deployed it at the competition. We then evaluated its performance against human expert assignments using blinded match-quality scores from judges on $309$ judge-venture pairs. Using a Mann-Whitney U statistic based test, we found no statistically significant difference in assignment quality between the two approaches ($AUC=0.48, p=0.40$); on average, algorithmic matches are rated $3.90$ and manual matches $3.94$ on a 5-point scale, where 5 indicates an excellent match. Furthermore, manual assignments that previously required a full week could be automated in several hours by the algorithm during deployment. These results demonstrate that HLSE achieves human-expert-level matching quality while offering greater scalability and efficiency, underscoring the potential of AI-driven solutions to support and enhance human decision-making for judge assignment in high-stakes settings.
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            <a href="https://www.alphaxiv.org/abs/2510.12637v1" target="_blank" rel="noopener noreferrer">
                COSTAR-A：一种用于提升大型语言模型在观点类问题上性能的提示框架
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            COSTAR-A: A prompting framework for enhancing Large Language Model performance on Point-of-View questions
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Nzubechukwu C. Ohalete, Kevin B. Gittner, Lauren M. Matheny
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注提示工程和LLM在观点类问题上的性能提升，这属于纯粹的LLM优化技术。虽然提示框架可能间接影响搜索中的问答质量，但缺乏明确的推荐系统、搜索或广告应用场景，且不涉及核心架构创新或异构数据处理。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 15:31:21
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12637v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12637v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">I.2.7</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models (LLMs) are highly sensitive to prompt design, and making optimized prompting techniques is crucial for generating consistent, high-quality outputs. In this study, we introduce COSTAR-A, a novel prompt engineering framework that enhances the existing COSTAR method, which stands for Context, Objective, Style, Tone, Audience, and Response, by adding the 'Answer' component at the end. We demonstrate that while the original COSTAR framework improves prompt clarity and aligns outputs for larger LLMs, its performance is less consistent with smaller, locally optimized models, particularly in tasks that require more directive or constrained outputs. Through a series of controlled prompt-output assessments with smaller (at most 8 billion parameters), fine-tuned models, we found that COSTAR-A can enhance the output structure and decisiveness of localized LLMs for certain tasks, although its effectiveness varies across models and use cases. Notably, the Llama 3.1-8B model exhibited performance improvements when prompted with COSTAR-A compared to COSTAR alone. These findings emphasize the adaptability and scalability of COSTAR-A as a prompting framework, particularly in computationally efficient AI deployments on resource-constrained hardware.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.12608v1" target="_blank" rel="noopener noreferrer">
                StyleDecipher：基于风格分析的鲁棒且可解释的LLM生成文本检测方法
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            StyleDecipher: Robust and Explainable Detection of LLM-Generated Texts with Stylistic Analysis
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Siyuan Li, Aodu Wulianghai, Xi Lin, Guangyan Li, Xiang Chen, Jun Wu, Jianhua Li
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM生成文本的检测和识别，属于内容真实性验证范畴，与推荐系统、搜索或广告的核心排序和匹配任务关联度较低。虽然检测技术可能间接应用于内容质量评估，但论文焦点更偏向安全性和内容验证，而非直接提升推荐、搜索或广告系统的核心性能。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 15:07:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12608v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12608v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    With the increasing integration of large language models (LLMs) into open-domain writing, detecting machine-generated text has become a critical task for ensuring content authenticity and trust. Existing approaches rely on statistical discrepancies or model-specific heuristics to distinguish between LLM-generated and human-written text. However, these methods struggle in real-world scenarios due to limited generalization, vulnerability to paraphrasing, and lack of explainability, particularly when facing stylistic diversity or hybrid human-AI authorship. In this work, we propose StyleDecipher, a robust and explainable detection framework that revisits LLM-generated text detection using combined feature extractors to quantify stylistic differences. By jointly modeling discrete stylistic indicators and continuous stylistic representations derived from semantic embeddings, StyleDecipher captures distinctive style-level divergences between human and LLM outputs within a unified representation space. This framework enables accurate, explainable, and domain-agnostic detection without requiring access to model internals or labeled segments. Extensive experiments across five diverse domains, including news, code, essays, reviews, and academic abstracts, demonstrate that StyleDecipher consistently achieves state-of-the-art in-domain accuracy. Moreover, in cross-domain evaluations, it surpasses existing baselines by up to 36.30%, while maintaining robustness against adversarial perturbations and mixed human-AI content. Further qualitative and quantitative analysis confirms that stylistic signals provide explainable evidence for distinguishing machine-generated text. Our source code can be accessed at https://github.com/SiyuanLi00/StyleDecipher.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.12548v1" target="_blank" rel="noopener noreferrer">
                VISaGE：理解视觉通用概念与例外情况
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            VISaGE: Understanding Visual Generics and Exceptions
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Stella Frank, Emily Allaway
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉通用概念和例外情况的理解，这属于纯粹的视觉理解领域，与推荐系统、搜索或广告的核心技术没有直接关联。虽然视觉语言模型的类比在焦点中有所提及，但该论文似乎更侧重于纯粹的视觉认知问题，而非将异构数据作为不同模态进行统一建模的具体应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 14:13:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12548v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12548v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.CV</span></div>
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                    While Vision Language Models (VLMs) learn conceptual representations, in the form of generalized knowledge, during training, they are typically used to analyze individual instances. When evaluation instances are atypical, this paradigm results in tension between two priors in the model. The first is a pragmatic prior that the textual and visual input are both relevant, arising from VLM finetuning on congruent inputs; the second is a semantic prior that the conceptual representation is generally true for instances of the category. In order to understand how VLMs trade off these priors, we introduce a new evaluation dataset, VISaGE, consisting of both typical and exceptional images. In carefully balanced experiments, we show that conceptual understanding degrades when the assumption of congruency underlying the pragmatic prior is violated with incongruent images. This effect is stronger than the effect of the semantic prior when querying about individual instances.
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            <a href="https://www.alphaxiv.org/abs/2510.12516v1" target="_blank" rel="noopener noreferrer">
                BoN Appetit团队在LeWiDi-2025：最佳N测试时扩展尚无法消化标注分歧
            </a>
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        <div class="mb-2 text-base text-gray-700">
            BoN Appetit Team at LeWiDi-2025: Best-of-N Test-time Scaling Can Not Stomach Annotation Disagreements (Yet)
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tomas Ruiz, Siyao Peng, Barbara Plank, Carsten Schwemmer
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注测试时扩展方法和标注分歧问题，这属于LLM评估和基准测试范畴，与我的核心关注点（推荐系统、搜索、广告的直接应用或使能技术）相关性较弱。虽然最佳N采样可能在某些场景下用于推荐多样性，但论文焦点是标注分歧而非实际应用，因此得分较低。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 13:43:08
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12516v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12516v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    Test-time scaling is a family of techniques to improve LLM outputs at inference time by performing extra computation. To the best of our knowledge, test-time scaling has been limited to domains with verifiably correct answers, like mathematics and coding. We transfer test-time scaling to the LeWiDi-2025 tasks to evaluate annotation disagreements. We experiment with three test-time scaling methods: two benchmark algorithms (Model Averaging and Majority Voting), and a Best-of-N sampling method. The two benchmark methods improve LLM performance consistently on the LeWiDi tasks, but the Best-of-N method does not. Our experiments suggest that the Best-of-N method does not currently transfer from mathematics to LeWiDi tasks, and we analyze potential reasons for this gap.
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            <a href="https://www.alphaxiv.org/abs/2510.12463v1" target="_blank" rel="noopener noreferrer">
                资源敏感但语言盲区：社区规模而非语法复杂性更能预测大型语言模型在新型Wug测试中的准确性
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            Resource-sensitive but language-blind: Community size and not grammatical complexity better predicts the accuracy of Large Language Models in a novel Wug Test
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Nikoleta Pantelidou, Evelina Leivada, Paolo Morosi
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究LLM在语言测试中的表现与社区规模的关系，属于纯语言学和模型评估范畴。虽然涉及LLM性能分析，但缺乏与推荐系统、搜索或广告的直接关联，也没有探讨Transformer架构改进或异构数据建模等核心技术方向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 12:52:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12463v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12463v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The linguistic abilities of Large Language Models are a matter of ongoing debate. This study contributes to this discussion by investigating model performance in a morphological generalization task that involves novel words. Using a multilingual adaptation of the Wug Test, six models were tested across four partially unrelated languages (Catalan, English, Greek, and Spanish) and compared with human speakers. The aim is to determine whether model accuracy approximates human competence and whether it is shaped primarily by linguistic complexity or by the quantity of available training data. Consistent with previous research, the results show that the models are able to generalize morphological processes to unseen words with human-like accuracy. However, accuracy patterns align more closely with community size and data availability than with structural complexity, refining earlier claims in the literature. In particular, languages with larger speaker communities and stronger digital representation, such as Spanish and English, revealed higher accuracy than less-resourced ones like Catalan and Greek. Overall, our findings suggest that model behavior is mainly driven by the richness of linguistic resources rather than by sensitivity to grammatical complexity, reflecting a form of performance that resembles human linguistic competence only superficially.
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            <a href="https://www.alphaxiv.org/abs/2510.12460v1" target="_blank" rel="noopener noreferrer">
                探究潜在知识冲突以实现忠实的检索增强生成
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            Probing Latent Knowledge Conflict for Faithful Retrieval-Augmented Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Linfeng Gao, Baolong Bi, Zheng Yuan, Le Wang, Zerui Chen, Zhimin Wei, Shenghua L...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注检索增强生成(RAG)中的知识冲突和忠实性问题，这属于纯粹的NLP评估和幻觉相关主题。虽然检索技术可能与搜索系统相关，但论文的核心焦点是确保生成内容的忠实性，这超出了当前关注的核心推荐系统、搜索排名或广告技术领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 12:48:24
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12460v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12460v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm to enhance the factuality of Large Language Models (LLMs). However, existing RAG systems often suffer from an unfaithfulness issue, where the model's response contradicts evidence from the retrieved context. Existing approaches to improving contextual faithfulness largely rely on external interventions, such as prompt engineering, decoding constraints, or reward-based fine-tuning. These works treat the LLM as a black box and overlook a crucial question: how does the LLM internally integrate retrieved evidence with its parametric memory, particularly under knowledge conflicts? To address this gap, we conduct a probing-based analysis of hidden-state representations in LLMs and observe three findings: knowledge integration occurs hierarchically, conflicts manifest as latent signals at the sentence level, and irrelevant context is often amplified when aligned with parametric knowledge. Building on these findings, we propose CLEAR (Conflict-Localized and Enhanced Attention for RAG), a framework that (i) decomposes context into fine-grained sentence-level knowledge, (ii) employs hidden-state probing to localize conflicting knowledge, and (iii) introduces conflict-aware fine-tuning to guide the model to accurately integrate retrieved evidence. Extensive experiments across three benchmarks demonstrate that CLEAR substantially improves both accuracy and contextual faithfulness, consistently outperforming strong baselines under diverse conflict conditions. The related resources are available at https://github.com/LinfengGao/CLEAR.
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            <a href="https://www.alphaxiv.org/abs/2510.12389v1" target="_blank" rel="noopener noreferrer">
                分词差异作为基础设施偏见：子词系统如何导致LLM访问和效率的不平等
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Tokenization Disparities as Infrastructure Bias: How Subword Systems Create Inequities in LLM Access and Efficiency
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hailay Kidu Teklehaymanot, Wolfgang Nejdl
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注分词系统的公平性和访问不平等问题，这属于伦理和公平性范畴，在无关主题中明确排除。虽然涉及LLM基础设施，但焦点是偏见和公平性影响，而非技术效率或架构改进，与推荐系统、搜索或广告的技术核心进展无关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 11:14:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12389v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12389v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">I.2.7; I.2.1; H.3.3; F.2.2</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Tokenization disparities pose a significant barrier to achieving equitable access to artificial intelligence across linguistically diverse populations. This study conducts a large-scale cross-linguistic evaluation of tokenization efficiency in over 200 languages to systematically quantify computational inequities in large language models (LLMs). Using a standardized experimental framework, we applied consistent preprocessing and normalization protocols, followed by uniform tokenization through the tiktoken library across all language samples. Comprehensive tokenization statistics were collected using established evaluation metrics, including Tokens Per Sentence (TPS) and Relative Tokenization Cost (RTC), benchmarked against English baselines. Our cross-linguistic analysis reveals substantial and systematic disparities: Latin-script languages consistently exhibit higher tokenization efficiency, while non-Latin and morphologically complex languages incur significantly greater token inflation, often 3-5 times higher RTC ratios. These inefficiencies translate into increased computational costs and reduced effective context utilization for underrepresented languages. Overall, the findings highlight structural inequities in current AI systems, where speakers of low-resource and non-Latin languages face disproportionate computational disadvantages. Future research should prioritize the development of linguistically informed tokenization strategies and adaptive vocabulary construction methods that incorporate typological diversity, ensuring more inclusive and computationally equitable multilingual AI systems.
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            <a href="https://www.alphaxiv.org/abs/2510.12306v1" target="_blank" rel="noopener noreferrer">
                基于大语言模型的自动语料标注大规模无监督流程：英语consider结构的变异与演变
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            A large-scale, unsupervised pipeline for automatic corpus annotation using LLMs: variation and change in the English consider construction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Cameron Morin, Matti Marttinen Larsson
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注使用LLMs进行语料库标注的语言学应用，属于NLP领域的特定任务。虽然涉及LLMs技术，但其应用场景（语言学语料标注）与推荐系统、搜索或广告领域没有直接关联，且论文焦点是语言结构分析而非推荐或搜索相关的技术进展。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 09:06:14
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12306v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12306v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    As natural language corpora expand at an unprecedented rate, manual annotation remains a significant methodological bottleneck in corpus linguistic work. We address this challenge by presenting a scalable, unsupervised pipeline for automating grammatical annotation in voluminous corpora using large language models (LLMs). Unlike previous supervised and iterative approaches, our method employs a four-phase workflow: prompt engineering, pre-hoc evaluation, automated batch processing, and post-hoc validation. We demonstrate the pipeline's accessibility and effectiveness through a diachronic case study of variation in the English consider construction. Using GPT-5 through the OpenAI API, we annotate 143,933 sentences from the Corpus of Historical American English (COHA) in under 60 hours, achieving 98%+ accuracy on two sophisticated annotation procedures. Our results suggest that LLMs can perform a range of data preparation tasks at scale with minimal human intervention, opening new possibilities for corpus-based research, though implementation requires attention to costs, licensing, and other ethical considerations.
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            <a href="https://www.alphaxiv.org/abs/2510.12195v1" target="_blank" rel="noopener noreferrer">
                用于同步语音翻译中分割任务的DPO调优大语言模型
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            DPO-Tuned Large Language Models for Segmentation in Simultaneous Speech Translation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zeyu Yang, Satoshi Nakamura
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于语音翻译中的分割任务，属于语音处理领域，与推荐系统、搜索或广告的核心关注点没有直接关联。虽然提到了DPO调优和LLMs，但这些技术应用在语音翻译场景中，缺乏明确的RecSys/Search/Ads应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 06:41:36
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                <a href="https://arxiv.org/abs/2510.12195v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12195v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Simultaneous speech translation requires accurate segmentation to balance translation quality and latency. Recent studies such as SHAS have introduced pretrained segmentation models, achieving stronger performance than heuristic rules. However, segmentation models such as SHAS, though pretrained and more robust than heuristic methods, are still constrained by supervised learning objectives and do not incorporate human preference alignment, which is crucial for natural real-time interpretation. In this work, we propose a segmentation framework based on large language models (LLMs) trained with Direct Preference Optimization (DPO). By leveraging preference alignment, our method enables LLMs to predict natural segmentation points that better meet the demands of real-time translation. We evaluate the system on the ACL 60/60 corpus across three language pairs (English-Japanese, Chinese, German), using SeamlessM4T v2 as the translation backbone. Experimental results show that our DPO-tuned LLM achieves higher segmentation accuracy than SHAS and yields consistent improvements in translation quality (BLEU, COMET) as well as latency (Average Lagging). Furthermore, our system benefits from IWSLT baselines for direct comparison. These findings highlight the potential of preference-tuned LLMs to surpass existing pretrained segmentation models and advance adaptive, human-aligned simultaneous interpretation.
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            <a href="https://www.alphaxiv.org/abs/2510.12164v1" target="_blank" rel="noopener noreferrer">
                并行推理技术综述
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            A Survey on Parallel Reasoning
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ziqi Wang, Boye Niu, Zipeng Gao, Zhi Zheng, Tong Xu, Linghui Meng, Zhongli Li, J...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇关于并行推理的综述论文主要关注推理过程的并行化技术，属于通用AI推理优化领域。虽然推理效率对LLM应用有一定影响，但该论文没有明确聚焦于推荐系统、搜索或广告领域的特定应用场景，也没有涉及Transformer架构改进或其他核心领域进展。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 05:42:19
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                <a href="https://arxiv.org/abs/2510.12164v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12164v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    With the increasing capabilities of Large Language Models (LLMs), parallel reasoning has emerged as a new inference paradigm that enhances reasoning robustness by concurrently exploring multiple lines of thought before converging on a final answer. It has become a significant trend to explore parallel reasoning to overcome the fragility of standard sequential methods and improve practical performance. In this paper, we aim to survey and summarize the progress and challenges of parallel reasoning. We first present a formal definition of parallel reasoning and clarify its distinction from related concepts like Chain-of-Thought. Then, we organize and discuss advanced techniques based on a novel taxonomy, including non-interactive reasoning, interactive reasoning, and efficiency-focused decoding strategies. Additionally, we explore various application scenarios, such as solving complex problems and enhancing the reliability of LLM outputs.Finally, we highlight the core challenges of parallel reasoning and suggest potential directions for future research. We hope that our work can provide a useful roadmap for beginners and encourage more research on improving parallel reasoning methods. Related source can be avaliable in https://github.com/PPPP-kaqiu/Awesome-Parallel-Reasoning.
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            <a href="https://www.alphaxiv.org/abs/2510.12137v1" target="_blank" rel="noopener noreferrer">
                Credal Transformer：一种量化与缓解大语言模型幻觉的原则性方法
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            Credal Transformer: A Principled Approach for Quantifying and Mitigating Hallucinations in Large Language Models
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shihao Ji, Zihui Song, Jiajie Huang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM的幻觉问题，这属于纯粹的NLP中心话题，被明确列为无关主题。虽然Transformer架构改进可能具有潜在应用价值，但论文专注于幻觉缓解而非架构效率或新注意力机制，与推荐系统、搜索或广告的核心技术需求关联度极低。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 04:31:49
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12137v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12137v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models (LLMs) hallucinate, generating factually incorrect yet confident assertions. We argue this stems from the Transformer's Softmax function, which creates "Artificial Certainty" by collapsing ambiguous attention scores into a single probability distribution, discarding uncertainty information at each layer. To fix this, we introduce the Credal Transformer, which replaces standard attention with a Credal Attention Mechanism (CAM) based on evidential theory. CAM produces a "credal set" (a set of distributions) instead of a single attention vector, with the set's size directly measuring model uncertainty. We implement this by re-conceptualizing attention scores as evidence masses for a Dirichlet distribution: sufficient evidence recovers standard attention, while insufficient evidence yields a diffuse distribution, representing ambiguity. Empirically, the Credal Transformer identifies out-of-distribution inputs, quantifies ambiguity, and significantly reduces confident errors on unanswerable questions by abstaining. Our contribution is a new architecture to mitigate hallucinations and a design paradigm that integrates uncertainty quantification directly into the model, providing a foundation for more reliable AI.
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            <a href="https://www.alphaxiv.org/abs/2510.12116v1" target="_blank" rel="noopener noreferrer">
                理解模态鸿沟：大型语音语言模型语音-文本对齐机制的实证研究
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            Understanding the Modality Gap: An Empirical Study on the Speech-Text Alignment Mechanism of Large Speech Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Bajian Xiang, Shuaijiang Zhao, Tingwei Guo, Wei Zou
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究语音-文本模态对齐机制，虽然涉及多模态建模概念，但其核心聚焦于语音模态，这在用户当前关注点中被明确列为不相关主题。语音模态与推荐系统、搜索或广告的核心技术栈关联度极低，缺乏明确的潜在应用场景。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 03:34:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12116v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12116v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    End-to-end Large Speech Language Models (LSLMs) have demonstrated impressive conversational generation abilities, yet consistently fall short of traditional pipeline systems on semantic understanding benchmarks. In this work, we reveal through systematic experimentation that although LSLMs lose some text input performance after speech-text alignment training, the performance gap between speech and text inputs is more pronounced, which we refer to as the modality gap. To understand this gap, we analyze both coarse- and fine-grained text and speech representations. At the coarse-grained level, representations of speech and text in deeper layers are found to be increasingly aligned in direction (cosine similarity), while concurrently diverging in magnitude (Euclidean distance). We further find that representation similarity is strongly correlated with the modality gap. At the fine-grained level, a spontaneous token-level alignment pattern between text and speech representations is observed. Based on this, we introduce the Alignment Path Score to quantify token-level alignment quality, which exhibits stronger correlation with the modality gap. Building on these insights, we design targeted interventions on critical tokens through angle projection and length normalization. These strategies demonstrate the potential to improve correctness for speech inputs. Our study provides the first systematic empirical analysis of the modality gap and alignment mechanisms in LSLMs, offering both theoretical and methodological guidance for future optimization.
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            <a href="https://www.alphaxiv.org/abs/2510.12115v1" target="_blank" rel="noopener noreferrer">
                领域适应中多语言知识获取动态的追踪：以英语-日语生物医学适应为例
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Tracing Multilingual Knowledge Acquisition Dynamics in Domain Adaptation: A Case Study of English-Japanese Biomedical Adaptation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xin Zhao, Naoki Yoshinaga, Yuma Tsuta, Akiko Aizawa
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注生物医学领域的多语言领域适应，这属于特定领域应用（医学/生物学），与我的核心关注点（推荐系统、搜索、广告）无关。虽然涉及领域适应技术，但缺乏明确的RecSys/Search/Ads应用潜力，且生物医学领域被明确列为不相关主题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 03:34:17
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12115v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12115v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Multilingual domain adaptation (ML-DA) is widely used to learn new domain knowledge across languages into large language models (LLMs). Although many methods have been proposed to improve domain adaptation, the mechanisms of multilingual knowledge acquisition, how domain knowledge is learned within a language and transferred across languages, remain underexplored. This gap leads to suboptimal performance, particularly in low-resource settings. This work examines the learning dynamics of LLMs during ML-DA. Because prior ML-DA studies often train and evaluate on datasets with mismatched knowledge coverage, we propose AdaXEval, an adaptive evaluation method that builds multiple-choice QA datasets from the same bilingual domain corpus used for training, thereby directly studying multilingual knowledge acquisition. Through continual training of LLMs with diverse data recipes, we track how LLMs acquire domain facts and pinpoint the mechanism behind the transformation process from domain training data to knowledge. Our experiments on a 13B English-Japanese bilingual LLM reveal that cross-lingual transfer remains challenging despite a high-quality bilingual corpus. The code has been released.
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            <a href="https://www.alphaxiv.org/abs/2510.12088v1" target="_blank" rel="noopener noreferrer">
                一生一次学习：从无引导探索中推断随机环境的符号世界模型
            </a>
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        <div class="mb-2 text-base text-gray-700">
            One Life to Learn: Inferring Symbolic World Models for Stochastic Environments from Unguided Exploration
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zaid Khan, Archiki Prasad, Elias Stengel-Eskin, Jaemin Cho, Mohit Bansal
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注从无监督探索中学习符号世界模型，这属于强化学习和环境建模领域。虽然环境建模在理论上可能与推荐系统中的用户行为建模相关，但论文专注于随机环境中的符号推理，缺乏与推荐、搜索或广告系统的直接联系，也没有涉及LLM、Transformer或多模态建模等核心技术。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 02:49:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12088v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12088v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Symbolic world modeling requires inferring and representing an environment's transitional dynamics as an executable program. Prior work has focused on largely deterministic environments with abundant interaction data, simple mechanics, and human guidance. We address a more realistic and challenging setting, learning in a complex, stochastic environment where the agent has only "one life" to explore a hostile environment without human guidance. We introduce OneLife, a framework that models world dynamics through conditionally-activated programmatic laws within a probabilistic programming framework. Each law operates through a precondition-effect structure, activating in relevant world states. This creates a dynamic computation graph that routes inference and optimization only through relevant laws, avoiding scaling challenges when all laws contribute to predictions about a complex, hierarchical state, and enabling the learning of stochastic dynamics even with sparse rule activation. To evaluate our approach under these demanding constraints, we introduce a new evaluation protocol that measures (a) state ranking, the ability to distinguish plausible future states from implausible ones, and (b) state fidelity, the ability to generate future states that closely resemble reality. We develop and evaluate our framework on Crafter-OO, our reimplementation of the Crafter environment that exposes a structured, object-oriented symbolic state and a pure transition function that operates on that state alone. OneLife can successfully learn key environment dynamics from minimal, unguided interaction, outperforming a strong baseline on 16 out of 23 scenarios tested. We also test OneLife's planning ability, with simulated rollouts successfully identifying superior strategies. Our work establishes a foundation for autonomously constructing programmatic world models of unknown, complex environments.
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            <a href="https://www.alphaxiv.org/abs/2510.12032v1" target="_blank" rel="noopener noreferrer">
                用于缓解大语言模型幻觉的多阶段提示优化方法
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Multi-stage Prompt Refinement for Mitigating Hallucinations in Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jung-Woo Shim, Yeong-Joon Ju, Ji-Hoon Park, Seong-Whan Lee
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM幻觉缓解这一纯NLP中心主题，属于用户明确排除的无关主题范畴。虽然提示优化技术可能在某些场景下对推荐或搜索系统有间接价值，但论文核心聚焦于幻觉问题而非其在推荐/搜索/广告领域的直接应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 00:31:36
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12032v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12032v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent advancements in large language models (LLMs) have shown strong performance in natural language understanding and generation tasks. However, LLMs continue to encounter challenges with hallucinations, where models generate plausible but incorrect information. While several factors contribute to hallucinations, the impact of ill-formed prompts, prompts with ambiguous wording, incorrect grammar, or incomplete information, was relatively under explored. To address this, we introduce Multi-stage Prompt Refinement (MPR), a framework designed to systematically improve these ill-formed prompts across multiple stages. Each stage addresses specific errors such as punctuation, typographical mistakes, and misuse of key terms, using small language models (SLMs) fine-tuned for these tasks. MPR iteratively enhances the clarity of prompts with additional context and employs a self-reflection mechanism with ranking to prioritize the most relevant input. Experimental results on hallucination benchmarks show that prompts refined by MPR achieve over an 85~\% win rate compared to their original forms, demonstrating its effectiveness in reducing hallucinations and improving LLM output accuracy. Interestingly, we reveal that MPR can be combined with existing post-hoc hallucination mitigation frameworks, further enhancing its versatility. MPR provides a lightweight and adaptable solution for enhancing LLM reliability across various domains.
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            <a href="https://www.alphaxiv.org/abs/2510.12029v1" target="_blank" rel="noopener noreferrer">
                CPR：通过治疗性提示精炼缓解大语言模型幻觉
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        <div class="mb-2 text-base text-gray-700">
            CPR: Mitigating Large Language Model Hallucinations with Curative Prompt Refinement
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jung-Woo Shim, Yeong-Joon Ju, Ji-Hoon Park, Seong-Whan Lee
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM幻觉缓解，这属于纯粹的NLP中心主题，被明确列为不相关主题。虽然提示精炼技术可能间接影响搜索质量，但论文核心焦点是幻觉问题本身，而非在推荐系统、搜索或广告中的具体应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 00:27:46
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12029v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12029v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent advancements in large language models (LLMs) highlight their fluency in generating responses to diverse prompts. However, these models sometimes generate plausible yet incorrect ``hallucinated" facts, undermining trust. A frequent but often overlooked cause of such errors is the use of poorly structured or vague prompts by users, leading LLMs to base responses on assumed rather than actual intentions. To mitigate hallucinations induced by these ill-formed prompts, we introduce Curative Prompt Refinement (CPR), a plug-and-play framework for curative prompt refinement that 1) cleans ill-formed prompts, and 2) generates additional informative task descriptions to align the intention of the user and the prompt using a fine-tuned small language model. When applied to language models, we discover that CPR significantly increases the quality of generation while also mitigating hallucination. Empirical studies show that prompts with CPR applied achieves over a 90\% win rate over the original prompts without any external knowledge.
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            <a href="https://www.alphaxiv.org/abs/2510.12023v1" target="_blank" rel="noopener noreferrer">
                对话转录文本的信息抽取：神经符号方法 vs 大语言模型
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Information Extraction from Conversation Transcripts: Neuro-Symbolic vs. LLM
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Alice Saebom Kwak, Maria Alexeeva, Gus Hahn-Powell, Keith Alcock, Kevin McLaughl...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要比较神经符号方法和LLM在对话信息抽取中的表现，属于通用NLP任务范畴。虽然信息抽取技术理论上可以应用于搜索中的查询理解或推荐中的用户意图识别，但论文本身专注于对话转录这一特定场景，与RecSys/Search/Ads的核心领域进展或直接应用关联较弱。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 00:10:24
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12023v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12023v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The current trend in information extraction (IE) is to rely extensively on large language models, effectively discarding decades of experience in building symbolic or statistical IE systems. This paper compares a neuro-symbolic (NS) and an LLM-based IE system in the agricultural domain, evaluating them on nine interviews across pork, dairy, and crop subdomains. The LLM-based system outperforms the NS one (F1 total: 69.4 vs. 52.7; core: 63.0 vs. 47.2), where total includes all extracted information and core focuses on essential details. However, each system has trade-offs: the NS approach offers faster runtime, greater control, and high accuracy in context-free tasks but lacks generalizability, struggles with contextual nuances, and requires significant resources to develop and maintain. The LLM-based system achieves higher performance, faster deployment, and easier maintenance but has slower runtime, limited control, model dependency and hallucination risks. Our findings highlight the "hidden cost" of deploying NLP systems in real-world applications, emphasizing the need to balance performance, efficiency, and control.
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            <a href="https://www.alphaxiv.org/abs/2510.12793v1" target="_blank" rel="noopener noreferrer">
                ViCO：一种面向语义感知动态高分辨率的训练策略
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            ViCO: A Training Strategy towards Semantic Aware Dynamic High-Resolution
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Long Cui, Weiyun Wang, Jie Shao, Zichen Wen, Gen Luo, Linfeng Zhang, Yanting Zha...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题暗示了计算机视觉领域的训练策略，可能涉及分辨率调整或视觉表示学习。虽然标题提到“语义感知”，但缺乏与推荐系统、搜索或广告领域的明确联系。没有证据表明该方法适用于异构数据建模或Transformer架构改进。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 17:58:10
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12793v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12793v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Existing Multimodal Large Language Models (MLLMs) suffer from increased inference costs due to the additional vision tokens introduced by image inputs. In this work, we propose Visual Consistency Learning (ViCO), a novel training algorithm that enables the model to represent images of varying semantic complexities using different numbers of vision tokens. The key idea behind our method is to employ multiple MLP connectors, each with a different image compression ratio, to downsample the vision tokens based on the semantic complexity of the image. During training, we minimize the KL divergence between the responses conditioned on different MLP connectors. At inference time, we introduce an image router, termed Visual Resolution Router (ViR), that automatically selects the appropriate compression rate for each image patch. Compared with existing dynamic high-resolution strategies, which adjust the number of visual tokens based on image resolutions, our method dynamically adapts the number of visual tokens according to semantic complexity. Experimental results demonstrate that our method can reduce the number of vision tokens by up to 50% while maintaining the model's perception, reasoning, and OCR capabilities. We hope this work will contribute to the development of more efficient MLLMs. The code and models will be released to facilitate future research.
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            <a href="https://www.alphaxiv.org/abs/2510.12789v1" target="_blank" rel="noopener noreferrer">
                UniFusion：视觉语言模型作为图像生成中的统一编码器
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
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            UniFusion: Vision-Language Model as Unified Encoder in Image Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kevin Li, Manuel Brack, Sudeep Katakol, Hareesh Ravi, Ajinkya Kale
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注图像生成领域的视觉语言模型应用，属于纯粹的视觉内容生成方向。虽然标题提到了统一编码器的概念，但其核心应用场景是图像生成而非推荐系统、搜索或广告中的排名任务，与当前关注点的相关性较弱。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 17:57:56
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12789v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12789v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Although recent advances in visual generation have been remarkable, most existing architectures still depend on distinct encoders for images and text. This separation constrains diffusion models' ability to perform cross-modal reasoning and knowledge transfer. Prior attempts to bridge this gap often use the last layer information from VLM, employ multiple visual encoders, or train large unified models jointly for text and image generation, which demands substantial computational resources and large-scale data, limiting its accessibility.We present UniFusion, a diffusion-based generative model conditioned on a frozen large vision-language model (VLM) that serves as a unified multimodal encoder. At the core of UniFusion is the Layerwise Attention Pooling (LAP) mechanism that extracts both high level semantics and low level details from text and visual tokens of a frozen VLM to condition a diffusion generative model. We demonstrate that LAP outperforms other shallow fusion architectures on text-image alignment for generation and faithful transfer of visual information from VLM to the diffusion model which is key for editing. We propose VLM-Enabled Rewriting Injection with Flexibile Inference (VERIFI), which conditions a diffusion transformer (DiT) only on the text tokens generated by the VLM during in-model prompt rewriting. VERIFI combines the alignment of the conditioning distribution with the VLM's reasoning capabilities for increased capabilities and flexibility at inference. In addition, finetuning on editing task not only improves text-image alignment for generation, indicative of cross-modality knowledge transfer, but also exhibits tremendous generalization capabilities. Our model when trained on single image editing, zero-shot generalizes to multiple image references further motivating the unified encoder design of UniFusion.
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            <a href="https://www.alphaxiv.org/abs/2510.12768v1" target="_blank" rel="noopener noreferrer">
                不确定性在用于单目4D重建的动态高斯溅射中至关重要
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Uncertainty Matters in Dynamic Gaussian Splatting for Monocular 4D Reconstruction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Fengzhi Guo, Chih-Chuan Hsu, Sihao Ding, Cheng Zhang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文主要关注计算机视觉中的4D重建技术，使用高斯溅射方法处理动态场景。虽然技术上先进，但与搜索、推荐或广告系统的核心领域进展、LLM技术应用或Transformer架构改进没有直接关联。该方法可能对某些特定场景的视觉理解有潜在价值，但与当前关注点的相关性非常有限。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 17:47:11
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12768v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12768v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.GR</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Reconstructing dynamic 3D scenes from monocular input is fundamentally under-constrained, with ambiguities arising from occlusion and extreme novel views. While dynamic Gaussian Splatting offers an efficient representation, vanilla models optimize all Gaussian primitives uniformly, ignoring whether they are well or poorly observed. This limitation leads to motion drifts under occlusion and degraded synthesis when extrapolating to unseen views. We argue that uncertainty matters: Gaussians with recurring observations across views and time act as reliable anchors to guide motion, whereas those with limited visibility are treated as less reliable. To this end, we introduce USplat4D, a novel Uncertainty-aware dynamic Gaussian Splatting framework that propagates reliable motion cues to enhance 4D reconstruction. Our key insight is to estimate time-varying per-Gaussian uncertainty and leverages it to construct a spatio-temporal graph for uncertainty-aware optimization. Experiments on diverse real and synthetic datasets show that explicitly modeling uncertainty consistently improves dynamic Gaussian Splatting models, yielding more stable geometry under occlusion and high-quality synthesis at extreme viewpoints.
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            <a href="https://www.alphaxiv.org/abs/2510.12712v1" target="_blank" rel="noopener noreferrer">
                超越视觉：评估基于工具的多模态大语言模型在图像感知、转换与推理任务上的表现
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        <div class="mb-2 text-base text-gray-700">
            Beyond Seeing: Evaluating Multimodal LLMs on Tool-Enabled Image Perception, Transformation, and Reasoning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xingang Guo, Utkarsh Tyagi, Advait Gosai, Paula Vergara, Ernesto Gabriel Hernánd...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注多模态LLM在图像处理工具上的评估，属于纯粹的视觉-语言模型评估范畴。虽然提到了工具增强能力，但核心焦点是图像感知和推理的基准测试，与推荐系统、搜索或广告中的异构数据处理没有直接关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 16:50:49
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12712v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12712v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Multimodal Large Language Models (MLLMs) are increasingly applied in real-world scenarios where user-provided images are often imperfect, requiring active image manipulations such as cropping, editing, or enhancement to uncover salient visual cues. Beyond static visual perception, MLLMs must also think with images: dynamically transforming visual content and integrating it with other tools to solve complex tasks. However, this shift from treating vision as passive context to a manipulable cognitive workspace remains underexplored. Most existing benchmarks still follow a think about images paradigm, where images are regarded as static inputs. To address this gap, we introduce IRIS, an Interactive Reasoning with Images and Systems that evaluates MLLMs' ability to perceive, transform, and reason across complex visual-textual tasks under the think with images paradigm. IRIS comprises 1,204 challenging, open-ended vision tasks (603 single-turn, 601 multi-turn) spanning across five diverse domains, each paired with detailed rubrics to enable systematic evaluation. Our evaluation shows that current MLLMs struggle with tasks requiring effective integration of vision and general-purpose tools. Even the strongest model (GPT-5-think) reaches only 18.68% pass rate. We further observe divergent tool-use behaviors, with OpenAI models benefiting from diverse image manipulations while Gemini-2.5-pro shows no improvement. By introducing the first benchmark centered on think with images, IRIS offers critical insights for advancing visual intelligence in MLLMs.
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            <a href="https://www.alphaxiv.org/abs/2510.12687v1" target="_blank" rel="noopener noreferrer">
                EReLiFM：面向噪声标签下开放集领域泛化的证据可靠性感知残差流元学习
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            EReLiFM: Evidential Reliability-Aware Residual Flow Meta-Learning for Open-Set Domain Generalization under Noisy Labels
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kunyu Peng, Di Wen, Kailun Yang, Jia Fu, Yufan Chen, Ruiping Liu, Jiamin Wu, Jun...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注噪声标签下的开放集领域泛化问题，虽然涉及元学习技术，但其核心焦点是噪声鲁棒性和领域泛化，与推荐系统、搜索或广告中的核心进展或LLM技术应用关联度较低。元学习技术本身在推荐系统中可能有潜在应用，但论文的具体技术方向（证据可靠性、残差流）与当前关注的核心领域进展或LLM使能技术缺乏直接联系。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 16:23:11
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12687v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12687v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span><span class="category-tag">cs.RO</span></div>
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                    Open-Set Domain Generalization (OSDG) aims to enable deep learning models to recognize unseen categories in new domains, which is crucial for real-world applications. Label noise hinders open-set domain generalization by corrupting source-domain knowledge, making it harder to recognize known classes and reject unseen ones. While existing methods address OSDG under Noisy Labels (OSDG-NL) using hyperbolic prototype-guided meta-learning, they struggle to bridge domain gaps, especially with limited clean labeled data. In this paper, we propose Evidential Reliability-Aware Residual Flow Meta-Learning (EReLiFM). We first introduce an unsupervised two-stage evidential loss clustering method to promote label reliability awareness. Then, we propose a residual flow matching mechanism that models structured domain- and category-conditioned residuals, enabling diverse and uncertainty-aware transfer paths beyond interpolation-based augmentation. During this meta-learning process, the model is optimized such that the update direction on the clean set maximizes the loss decrease on the noisy set, using pseudo labels derived from the most confident predicted class for supervision. Experimental results show that EReLiFM outperforms existing methods on OSDG-NL, achieving state-of-the-art performance. The source code is available at https://github.com/KPeng9510/ERELIFM.
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            <a href="https://www.alphaxiv.org/abs/2510.12586v1" target="_blank" rel="noopener noreferrer">
                通过自监督预训练推进端到端像素空间生成建模
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        <div class="mb-2 text-base text-gray-700">
            Advancing End-to-End Pixel Space Generative Modeling via Self-supervised Pre-training
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiachen Lei, Keli Liu, Julius Berner, Haiming Yu, Hongkai Zheng, Jiahong Wu, Xia...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于像素空间生成建模，属于计算机视觉领域的纯视觉生成任务，与推荐系统、搜索或广告的核心技术需求没有直接关联。虽然自监督预训练是通用技术，但论文明确聚焦于像素空间生成，这种视觉内容生成应用超出了当前关注的技术范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 14:41:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12586v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12586v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Pixel-space generative models are often more difficult to train and generally underperform compared to their latent-space counterparts, leaving a persistent performance and efficiency gap. In this paper, we introduce a novel two-stage training framework that closes this gap for pixel-space diffusion and consistency models. In the first stage, we pre-train encoders to capture meaningful semantics from clean images while aligning them with points along the same deterministic sampling trajectory, which evolves points from the prior to the data distribution. In the second stage, we integrate the encoder with a randomly initialized decoder and fine-tune the complete model end-to-end for both diffusion and consistency models. Our training framework demonstrates strong empirical performance on ImageNet dataset. Specifically, our diffusion model reaches an FID of 2.04 on ImageNet-256 and 2.35 on ImageNet-512 with 75 number of function evaluations (NFE), surpassing prior pixel-space methods by a large margin in both generation quality and efficiency while rivaling leading VAE-based models at comparable training cost. Furthermore, on ImageNet-256, our consistency model achieves an impressive FID of 8.82 in a single sampling step, significantly surpassing its latent-space counterpart. To the best of our knowledge, this marks the first successful training of a consistency model directly on high-resolution images without relying on pre-trained VAEs or diffusion models.
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                基于时间条件Mamba的人类运动学习
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            Learning Human Motion with Temporally Conditional Mamba
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Quang Nguyen, Tri Le, Baoru Huang, Minh Nhat Vu, Ngan Le, Thieu Vo, Anh Nguyen
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注人类运动建模，属于计算机视觉和运动分析领域，与推荐系统、搜索或广告的核心技术没有直接关联。虽然Mamba架构作为状态空间模型在序列建模方面有潜力，但该论文的应用场景（人类运动）与RecSys/Search/Ads领域相距甚远，缺乏明确的跨领域应用路径。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 14:29:51
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12573v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12573v1
                </a>
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Learning human motion based on a time-dependent input signal presents a challenging yet impactful task with various applications. The goal of this task is to generate or estimate human movement that consistently reflects the temporal patterns of conditioning inputs. Existing methods typically rely on cross-attention mechanisms to fuse the condition with motion. However, this approach primarily captures global interactions and struggles to maintain step-by-step temporal alignment. To address this limitation, we introduce Temporally Conditional Mamba, a new mamba-based model for human motion generation. Our approach integrates conditional information into the recurrent dynamics of the Mamba block, enabling better temporally aligned motion. To validate the effectiveness of our method, we evaluate it on a variety of human motion tasks. Extensive experiments demonstrate that our model significantly improves temporal alignment, motion realism, and condition consistency over state-of-the-art approaches. Our project page is available at https://zquang2202.github.io/TCM.
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            <a href="https://www.alphaxiv.org/abs/2510.12537v1" target="_blank" rel="noopener noreferrer">
                基于平衡分数扩散的无条件人体运动与形状生成
            </a>
        </h3>
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            <i class="fa fa-star mr-1"></i>2/10
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            Unconditional Human Motion and Shape Generation via Balanced Score-Based Diffusion
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>David Björkstrand, Tiesheng Wang, Lars Bretzner, Josephine Sullivan
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的人体运动生成，属于纯粹的视觉生成任务。虽然扩散模型是LLM相关的生成技术，但该工作没有展示与推荐系统、搜索或广告的明确联系。人体运动生成可能的应用场景如虚拟试衣或AR广告过于间接，不属于核心关注领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 14:02:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12537v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12537v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Recent work has explored a range of model families for human motion generation, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and diffusion-based models. Despite their differences, many methods rely on over-parameterized input features and auxiliary losses to improve empirical results. These strategies should not be strictly necessary for diffusion models to match the human motion distribution. We show that on par with state-of-the-art results in unconditional human motion generation are achievable with a score-based diffusion model using only careful feature-space normalization and analytically derived weightings for the standard L2 score-matching loss, while generating both motion and shape directly, thereby avoiding slow post hoc shape recovery from joints. We build the method step by step, with a clear theoretical motivation for each component, and provide targeted ablations demonstrating the effectiveness of each proposed addition in isolation.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.12422v1" target="_blank" rel="noopener noreferrer">
                VideoLucy：用于长视频理解的深度记忆回溯
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            <i class="fa fa-star mr-1"></i>2/10
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            VideoLucy: Deep Memory Backtracking for Long Video Understanding
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jialong Zuo, Yongtai Deng, Lingdong Kong, Jingkang Yang, Rui Jin, Yiwei Zhang, N...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">虽然该论文涉及视频理解，但主要关注长视频处理中的记忆回溯技术，这与推荐系统、搜索或广告的核心技术焦点关联较弱。其潜在的时序建模方法可能在用户行为序列分析中有间接应用，但缺乏明确的RecSys/Search/Ads应用场景。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 11:59:19
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12422v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12422v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Recent studies have shown that agent-based systems leveraging large language models (LLMs) for key information retrieval and integration have emerged as a promising approach for long video understanding. However, these systems face two major challenges. First, they typically perform modeling and reasoning on individual frames, struggling to capture the temporal context of consecutive frames. Second, to reduce the cost of dense frame-level captioning, they adopt sparse frame sampling, which risks discarding crucial information. To overcome these limitations, we propose VideoLucy, a deep memory backtracking framework for long video understanding. Inspired by the human recollection process from coarse to fine, VideoLucy employs a hierarchical memory structure with progressive granularity. This structure explicitly defines the detail level and temporal scope of memory at different hierarchical depths. Through an agent-based iterative backtracking mechanism, VideoLucy systematically mines video-wide, question-relevant deep memories until sufficient information is gathered to provide a confident answer. This design enables effective temporal understanding of consecutive frames while preserving critical details. In addition, we introduce EgoMem, a new benchmark for long video understanding. EgoMem is designed to comprehensively evaluate a model's ability to understand complex events that unfold over time and capture fine-grained details in extremely long videos. Extensive experiments demonstrate the superiority of VideoLucy. Built on open-source models, VideoLucy significantly outperforms state-of-the-art methods on multiple long video understanding benchmarks, achieving performance even surpassing the latest proprietary models such as GPT-4o. Our code and dataset will be made publicly at https://videolucy.github.io
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            <a href="https://www.alphaxiv.org/abs/2510.12400v1" target="_blank" rel="noopener noreferrer">
                迈向基于视觉语言模型的通用城市监控：综述、评估与研究议程
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            Towards General Urban Monitoring with Vision-Language Models: A Review, Evaluation, and a Research Agenda
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>André Torneiro, Diogo Monteiro, Paulo Novais, Pedro Rangel Henriques, Nuno F. Ro...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉和城市监控应用，属于纯粹的视觉领域研究。虽然涉及视觉语言模型技术，但其应用场景（城市监控）与推荐系统、搜索或广告领域没有直接关联，也不涉及处理异构数据或用户行为序列等推荐系统核心问题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 11:27:46
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12400v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12400v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Urban monitoring of public infrastructure (such as waste bins, road signs, vegetation, sidewalks, and construction sites) poses significant challenges due to the diversity of objects, environments, and contextual conditions involved. Current state-of-the-art approaches typically rely on a combination of IoT sensors and manual inspections, which are costly, difficult to scale, and often misaligned with citizens' perception formed through direct visual observation. This raises a critical question: Can machines now "see" like citizens and infer informed opinions about the condition of urban infrastructure? Vision-Language Models (VLMs), which integrate visual understanding with natural language reasoning, have recently demonstrated impressive capabilities in processing complex visual information, turning them into a promising technology to address this challenge. This systematic review investigates the role of VLMs in urban monitoring, with particular emphasis on zero-shot applications. Following the PRISMA methodology, we analyzed 32 peer-reviewed studies published between 2021 and 2025 to address four core research questions: (1) What urban monitoring tasks have been effectively addressed using VLMs? (2) Which VLM architectures and frameworks are most commonly used and demonstrate superior performance? (3) What datasets and resources support this emerging field? (4) How are VLM-based applications evaluated, and what performance levels have been reported?
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            <a href="https://www.alphaxiv.org/abs/2510.12387v1" target="_blank" rel="noopener noreferrer">
                场景坐标重建先验
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        <div class="mb-2 text-base text-gray-700">
            Scene Coordinate Reconstruction Priors
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wenjing Bian, Axel Barroso-Laguna, Tommaso Cavallari, Victor Adrian Prisacariu, ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及计算机视觉中的场景坐标重建技术，主要关注3D场景理解或SLAM领域。虽然场景理解在广义上可能与搜索推荐相关，但该标题缺乏明确的连接点，且属于纯粹的视觉技术范畴，与当前关注的推荐系统、搜索广告、LLM技术及Transformer架构等核心方向关联度极低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 11:13:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12387v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12387v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Scene coordinate regression (SCR) models have proven to be powerful implicit scene representations for 3D vision, enabling visual relocalization and structure-from-motion. SCR models are trained specifically for one scene. If training images imply insufficient multi-view constraints SCR models degenerate. We present a probabilistic reinterpretation of training SCR models, which allows us to infuse high-level reconstruction priors. We investigate multiple such priors, ranging from simple priors over the distribution of reconstructed depth values to learned priors over plausible scene coordinate configurations. For the latter, we train a 3D point cloud diffusion model on a large corpus of indoor scans. Our priors push predicted 3D scene points towards plausible geometry at each training step to increase their likelihood. On three indoor datasets our priors help learning better scene representations, resulting in more coherent scene point clouds, higher registration rates and better camera poses, with a positive effect on down-stream tasks such as novel view synthesis and camera relocalization.
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            <a href="https://www.alphaxiv.org/abs/2510.12385v1" target="_blank" rel="noopener noreferrer">
                通过时空建模学习识别自我中心装配视频中正确完成的步骤
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        <div class="mb-2 text-base text-gray-700">
            Learning to Recognize Correctly Completed Procedure Steps in Egocentric Assembly Videos through Spatio-Temporal Modeling
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tim J. Schoonbeek, Shao-Hsuan Hung, Dan Lehman, Hans Onvlee, Jacek Kustra, Peter...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于自我中心视角的装配视频分析，属于计算机视觉领域。虽然涉及步骤识别和时空建模，但其应用场景（装配视频）与推荐系统、搜索或广告的核心技术栈关联性较弱。论文的技术方法可能对理解用户行为序列有一定启发，但缺乏明确的RecSys/Search/Ads应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 11:03:30
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12385v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12385v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Procedure step recognition (PSR) aims to identify all correctly completed steps and their sequential order in videos of procedural tasks. The existing state-of-the-art models rely solely on detecting assembly object states in individual video frames. By neglecting temporal features, model robustness and accuracy are limited, especially when objects are partially occluded. To overcome these limitations, we propose Spatio-Temporal Occlusion-Resilient Modeling for Procedure Step Recognition (STORM-PSR), a dual-stream framework for PSR that leverages both spatial and temporal features. The assembly state detection stream operates effectively with unobstructed views of the object, while the spatio-temporal stream captures both spatial and temporal features to recognize step completions even under partial occlusion. This stream includes a spatial encoder, pre-trained using a novel weakly supervised approach to capture meaningful spatial representations, and a transformer-based temporal encoder that learns how these spatial features relate over time. STORM-PSR is evaluated on the MECCANO and IndustReal datasets, reducing the average delay between actual and predicted assembly step completions by 11.2% and 26.1%, respectively, compared to prior methods. We demonstrate that this reduction in delay is driven by the spatio-temporal stream, which does not rely on unobstructed views of the object to infer completed steps. The code for STORM-PSR, along with the newly annotated MECCANO labels, is made publicly available at https://timschoonbeek.github.io/stormpsr .
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            <a href="https://www.alphaxiv.org/abs/2510.12308v1" target="_blank" rel="noopener noreferrer">
                用于新颖城市视角合成的混合高斯泼溅方法
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            Hybrid Gaussian Splatting for Novel Urban View Synthesis
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mohamed Omran, Farhad Zanjani, Davide Abati, Jens Petersen, Amirhossein Habibian
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文主要关注计算机视觉中的新颖视角合成技术，特别是针对城市环境的3D重建和渲染。虽然高斯泼溅是计算机图形学中的一种高效渲染方法，但该工作主要面向纯粹的视觉应用，与推荐系统、搜索或广告的核心技术栈没有明确的直接关联。在推荐/搜索/广告领域可能的应用场景（如虚拟试穿、商品展示）过于间接且不是该技术的核心目标。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 09:09:13
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12308v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12308v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    This paper describes the Qualcomm AI Research solution to the RealADSim-NVS challenge, hosted at the RealADSim Workshop at ICCV 2025. The challenge concerns novel view synthesis in street scenes, and participants are required to generate, starting from car-centric frames captured during some training traversals, renders of the same urban environment as viewed from a different traversal (e.g. different street lane or car direction). Our solution is inspired by hybrid methods in scene generation and generative simulators merging gaussian splatting and diffusion models, and it is composed of two stages: First, we fit a 3D reconstruction of the scene and render novel views as seen from the target cameras. Then, we enhance the resulting frames with a dedicated single-step diffusion model. We discuss specific choices made in the initialization of gaussian primitives as well as the finetuning of the enhancer model and its training data curation. We report the performance of our model design and we ablate its components in terms of novel view quality as measured by PSNR, SSIM and LPIPS. On the public leaderboard reporting test results, our proposal reaches an aggregated score of 0.432, achieving the second place overall.
                </div>
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.12267v1" target="_blank" rel="noopener noreferrer">
                SpineBench：用于脊柱病理分析的多模态大语言模型基准测试
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            SpineBench: Benchmarking Multimodal LLMs for Spinal Pathology Analysis
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chenghanyu Zhang, Zekun Li, Peipei Li, Xing Cui, Shuhan Xia, Weixiang Yan, Yiqia...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学领域的脊柱病理分析基准测试，属于明确的医疗应用场景。虽然涉及多模态LLM技术，但其应用领域与推荐系统、搜索或广告完全无关，属于被明确排除的医疗生物学应用范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 08:19:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12267v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12267v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    With the increasing integration of Multimodal Large Language Models (MLLMs) into the medical field, comprehensive evaluation of their performance in various medical domains becomes critical. However, existing benchmarks primarily assess general medical tasks, inadequately capturing performance in nuanced areas like the spine, which relies heavily on visual input. To address this, we introduce SpineBench, a comprehensive Visual Question Answering (VQA) benchmark designed for fine-grained analysis and evaluation of MLLMs in the spinal domain. SpineBench comprises 64,878 QA pairs from 40,263 spine images, covering 11 spinal diseases through two critical clinical tasks: spinal disease diagnosis and spinal lesion localization, both in multiple-choice format. SpineBench is built by integrating and standardizing image-label pairs from open-source spinal disease datasets, and samples challenging hard negative options for each VQA pair based on visual similarity (similar but not the same disease), simulating real-world challenging scenarios. We evaluate 12 leading MLLMs on SpineBench. The results reveal that these models exhibit poor performance in spinal tasks, highlighting limitations of current MLLM in the spine domain and guiding future improvements in spinal medicine applications. SpineBench is publicly available at https://zhangchenghanyu.github.io/SpineBench.github.io/.
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            <a href="https://www.alphaxiv.org/abs/2510.12260v1" target="_blank" rel="noopener noreferrer">
                AngularFuse：基于角度感知的空间敏感多模态图像融合方法深入探究
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            AngularFuse: A Closer Look at Angle-based Perception for Spatial-Sensitive Multi-Modality Image Fusion
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xiaopeng Liu, Yupei Lin, Sen Zhang, Xiao Wang, Yukai Shi, Liang Lin
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注多模态图像融合技术，属于计算机视觉领域，与推荐系统、搜索或广告的核心技术栈关联度较低。虽然标题提到多模态融合，但其具体应用场景（空间敏感图像融合）更偏向视觉处理而非推荐/搜索中的异构数据建模，因此潜在应用价值有限。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 08:13:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12260v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12260v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span><span class="category-tag">eess.IV</span></div>
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                    Visible-infrared image fusion is crucial in key applications such as autonomous driving and nighttime surveillance. Its main goal is to integrate multimodal information to produce enhanced images that are better suited for downstream tasks. Although deep learning based fusion methods have made significant progress, mainstream unsupervised approaches still face serious challenges in practical applications. Existing methods mostly rely on manually designed loss functions to guide the fusion process. However, these loss functions have obvious limitations. On one hand, the reference images constructed by existing methods often lack details and have uneven brightness. On the other hand, the widely used gradient losses focus only on gradient magnitude. To address these challenges, this paper proposes an angle-based perception framework for spatial-sensitive image fusion (AngularFuse). At first, we design a cross-modal complementary mask module to force the network to learn complementary information between modalities. Then, a fine-grained reference image synthesis strategy is introduced. By combining Laplacian edge enhancement with adaptive histogram equalization, reference images with richer details and more balanced brightness are generated. Last but not least, we introduce an angle-aware loss, which for the first time constrains both gradient magnitude and direction simultaneously in the gradient domain. AngularFuse ensures that the fused images preserve both texture intensity and correct edge orientation. Comprehensive experiments on the MSRS, RoadScene, and M3FD public datasets show that AngularFuse outperforms existing mainstream methods with clear margin. Visual comparisons further confirm that our method produces sharper and more detailed results in challenging scenes, demonstrating superior fusion capability.
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            <a href="https://www.alphaxiv.org/abs/2510.12259v1" target="_blank" rel="noopener noreferrer">
                局部背景特征在分布外检测中至关重要
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Local Background Features Matter in Out-of-Distribution Detection
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jinlun Ye, Zhuohao Sun, Yiqiao Qiu, Qiu Li, Zhijun Tan, Ruixuan Wang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文主要关注计算机视觉领域的分布外检测问题，属于纯粹的视觉检测任务。虽然背景特征处理在推荐系统中可能有类比应用，但论文本身没有明确展示与推荐系统、搜索或广告的直接关联，也不涉及LLM技术或Transformer架构的改进。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 08:12:49
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12259v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12259v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Out-of-distribution (OOD) detection is crucial when deploying deep neural networks in the real world to ensure the reliability and safety of their applications. One main challenge in OOD detection is that neural network models often produce overconfident predictions on OOD data. While some methods using auxiliary OOD datasets or generating fake OOD images have shown promising OOD detection performance, they are limited by the high costs of data collection and training. In this study, we propose a novel and effective OOD detection method that utilizes local background features as fake OOD features for model training. Inspired by the observation that OOD images generally share similar background regions with ID images, the background features are extracted from ID images as simulated OOD visual representations during training based on the local invariance of convolution. Through being optimized to reduce the $L_2$-norm of these background features, the neural networks are able to alleviate the overconfidence issue on OOD data. Extensive experiments on multiple standard OOD detection benchmarks confirm the effectiveness of our method and its wide combinatorial compatibility with existing post-hoc methods, with new state-of-the-art performance achieved from our method.
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            <a href="https://www.alphaxiv.org/abs/2510.12256v1" target="_blank" rel="noopener noreferrer">
                基于分层时空一致代理嵌入的可轻松编辑的向量化视频表示
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Vectorized Video Representation with Easy Editing via Hierarchical Spatio-Temporally Consistent Proxy Embedding
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ye Chen, Liming Tan, Yupeng Zhu, Yuanbin Wang, Bingbing Ni
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视频表示和编辑技术，属于计算机视觉领域，与推荐系统、搜索或广告的核心技术关联性较弱。虽然向量化表示技术可能在某些多媒体推荐场景中有间接应用，但论文标题明确聚焦于视频编辑功能，这超出了当前关注的技术范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 08:05:30
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12256v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12256v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Current video representations heavily rely on unstable and over-grained priors for motion and appearance modelling, \emph{i.e.}, pixel-level matching and tracking. A tracking error of just a few pixels would lead to the collapse of the visual object representation, not to mention occlusions and large motion frequently occurring in videos. To overcome the above mentioned vulnerability, this work proposes spatio-temporally consistent proxy nodes to represent dynamically changing objects/scenes in the video. On the one hand, the hierarchical proxy nodes have the ability to stably express the multi-scale structure of visual objects, so they are not affected by accumulated tracking error, long-term motion, occlusion, and viewpoint variation. On the other hand, the dynamic representation update mechanism of the proxy nodes adequately leverages spatio-temporal priors of the video to mitigate the impact of inaccurate trackers, thereby effectively handling drastic changes in scenes and objects. Additionally, the decoupled encoding manner of the shape and texture representations across different visual objects in the video facilitates controllable and fine-grained appearance editing capability. Extensive experiments demonstrate that the proposed representation achieves high video reconstruction accuracy with fewer parameters and supports complex video processing tasks, including video in-painting and keyframe-based temporally consistent video editing.
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            <a href="https://www.alphaxiv.org/abs/2510.12231v1" target="_blank" rel="noopener noreferrer">
                BIGFix：基于令牌修复的双向图像生成
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            BIGFix: Bidirectional Image Generation with Token Fixing
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Victor Besnier, David Hurych, Andrei Bursuc, Eduardo Valle
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于图像生成技术，属于计算机视觉领域，与推荐系统、搜索或广告的核心技术栈关联性较弱。虽然图像生成技术可能间接应用于广告创意生成等场景，但这属于明确的非相关主题范畴，因此整体相关性较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 07:34:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12231v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12231v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Recent advances in image and video generation have raised significant interest from both academia and industry. A key challenge in this field is improving inference efficiency, as model size and the number of inference steps directly impact the commercial viability of generative models while also posing fundamental scientific challenges. A promising direction involves combining auto-regressive sequential token modeling with multi-token prediction per step, reducing inference time by up to an order of magnitude. However, predicting multiple tokens in parallel can introduce structural inconsistencies due to token incompatibilities, as capturing complex joint dependencies during training remains challenging. Traditionally, once tokens are sampled, there is no mechanism to backtrack and refine erroneous predictions. We propose a method for self-correcting image generation by iteratively refining sampled tokens. We achieve this with a novel training scheme that injects random tokens in the context, improving robustness and enabling token fixing during sampling. Our method preserves the efficiency benefits of parallel token prediction while significantly enhancing generation quality. We evaluate our approach on image generation using the ImageNet-256 and CIFAR-10 datasets, as well as on video generation with UCF-101 and NuScenes, demonstrating substantial improvements across both modalities.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.12208v1" target="_blank" rel="noopener noreferrer">
                合成数据对目标检测模型性能的影响：与真实世界数据的对比分析
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            The Impact of Synthetic Data on Object Detection Model Performance: A Comparative Analysis with Real-World Data
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Muammer Bay, Timo von Marcard, Dren Fazlija
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉领域的目标检测任务，与推荐系统、搜索或广告的核心技术关联度较低。虽然合成数据生成技术在某些场景下可能应用于数据增强，但论文本身并未明确展示在推荐、搜索或广告领域的直接应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 06:59:51
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12208v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12208v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent advances in generative AI, particularly in computer vision (CV), offer new opportunities to optimize workflows across industries, including logistics and manufacturing. However, many AI applications are limited by a lack of expertise and resources, which forces a reliance on general-purpose models. Success with these models often requires domain-specific data for fine-tuning, which can be costly and inefficient. Thus, using synthetic data for fine-tuning is a popular, cost-effective alternative to gathering real-world data. This work investigates the impact of synthetic data on the performance of object detection models, compared to models trained on real-world data only, specifically within the domain of warehouse logistics. To this end, we examined the impact of synthetic data generated using the NVIDIA Omniverse Replicator tool on the effectiveness of object detection models in real-world scenarios. It comprises experiments focused on pallet detection in a warehouse setting, utilizing both real and various synthetic dataset generation strategies. Our findings provide valuable insights into the practical applications of synthetic image data in computer vision, suggesting that a balanced integration of synthetic and real data can lead to robust and efficient object detection models.
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            <a href="https://www.alphaxiv.org/abs/2510.12190v1" target="_blank" rel="noopener noreferrer">
                基于行车记录仪视频的事故报告分层推理视觉语言模型
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Hierarchical Reasoning with Vision-Language Models for Incident Reports from Dashcam Videos
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shingo Yokoi, Kento Sasaki, Yu Yamaguchi
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉语言模型在行车记录仪视频事故报告中的特定应用，属于纯粹的视觉-语言多模态任务。虽然涉及视觉语言模型技术，但其应用场景（事故报告、行车视频）与推荐系统、搜索或广告领域没有直接关联，也不涉及处理推荐系统常见的异构数据模态。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 06:36:41
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12190v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12190v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Recent advances in end-to-end (E2E) autonomous driving have been enabled by training on diverse large-scale driving datasets, yet autonomous driving models still struggle in out-of-distribution (OOD) scenarios. The COOOL benchmark targets this gap by encouraging hazard understanding beyond closed taxonomies, and the 2COOOL challenge extends it to generating human-interpretable incident reports. We present a hierarchical reasoning framework for incident report generation from dashcam videos that integrates frame-level captioning, incident frame detection, and fine-grained reasoning within vision-language models (VLMs). We further improve factual accuracy and readability through model ensembling and a Blind A/B Scoring selection protocol. On the official 2COOOL open leaderboard, our method ranks 2nd among 29 teams and achieves the best CIDEr-D score, producing accurate and coherent incident narratives. These results indicate that hierarchical reasoning with VLMs is a promising direction for accident analysis and for broader understanding of safety-critical traffic events. The implementation and code are available at https://github.com/riron1206/kaggle-2COOOL-2nd-Place-Solution.
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            <a href="https://www.alphaxiv.org/abs/2510.12174v1" target="_blank" rel="noopener noreferrer">
                UniGS：面向多模态渲染的统一几何感知高斯溅射
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            UniGS: Unified Geometry-Aware Gaussian Splatting for Multimodal Rendering
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yusen Xie, Zhenmin Huang, Jianhao Jiao, Dimitrios Kanoulas, Jun Ma
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机图形学中的多模态渲染技术，属于纯粹的视觉/图形学领域，与推荐系统、搜索或广告的核心技术栈没有直接关联。虽然标题中提到'多模态'，但这指的是视觉渲染中的不同模态（如RGB、深度等），而非推荐系统中常见的用户行为、上下文特征等异构数据模态，因此潜在应用价值有限。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 06:07:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12174v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12174v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.RO</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    In this paper, we propose UniGS, a unified map representation and differentiable framework for high-fidelity multimodal 3D reconstruction based on 3D Gaussian Splatting. Our framework integrates a CUDA-accelerated rasterization pipeline capable of rendering photo-realistic RGB images, geometrically accurate depth maps, consistent surface normals, and semantic logits simultaneously. We redesign the rasterization to render depth via differentiable ray-ellipsoid intersection rather than using Gaussian centers, enabling effective optimization of rotation and scale attribute through analytic depth gradients. Furthermore, we derive the analytic gradient formulation for surface normal rendering, ensuring geometric consistency among reconstructed 3D scenes. To improve computational and storage efficiency, we introduce a learnable attribute that enables differentiable pruning of Gaussians with minimal contribution during training. Quantitative and qualitative experiments demonstrate state-of-the-art reconstruction accuracy across all modalities, validating the efficacy of our geometry-aware paradigm. Source code and multimodal viewer will be available on GitHub.
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            <a href="https://www.alphaxiv.org/abs/2510.12160v1" target="_blank" rel="noopener noreferrer">
                通过聚集和传播时空信息进行状态空间提示的视频理解
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            <i class="fa fa-star mr-1"></i>2/10
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            State Space Prompting via Gathering and Spreading Spatio-Temporal Information for Video Understanding
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiahuan Zhou, Kai Zhu, Zhenyu Cui, Zichen Liu, Xu Zou, Gang Hua
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于视频理解，属于纯粹的视觉领域应用，与推荐系统、搜索或广告没有明确关联。虽然状态空间模型和提示技术是LLM相关技术，但论文的应用场景局限于视频理解，没有展示在RecSys/Search/Ads领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 05:30:36
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12160v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12160v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recently, pre-trained state space models have shown great potential for video classification, which sequentially compresses visual tokens in videos with linear complexity, thereby improving the processing efficiency of video data while maintaining high performance. To apply powerful pre-trained models to downstream tasks, prompt learning is proposed to achieve efficient downstream task adaptation with only a small number of fine-tuned parameters. However, the sequentially compressed visual prompt tokens fail to capture the spatial and temporal contextual information in the video, thus limiting the effective propagation of spatial information within a video frame and temporal information between frames in the state compression model and the extraction of discriminative information. To tackle the above issue, we proposed a State Space Prompting (SSP) method for video understanding, which combines intra-frame and inter-frame prompts to aggregate and propagate key spatiotemporal information in the video. Specifically, an Intra-Frame Gathering (IFG) module is designed to aggregate spatial key information within each frame. Besides, an Inter-Frame Spreading (IFS) module is designed to spread discriminative spatio-temporal information across different frames. By adaptively balancing and compressing key spatio-temporal information within and between frames, our SSP effectively propagates discriminative information in videos in a complementary manner. Extensive experiments on four video benchmark datasets verify that our SSP significantly outperforms existing SOTA methods by 2.76% on average while reducing the overhead of fine-tuning parameters.
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            <a href="https://www.alphaxiv.org/abs/2510.12150v1" target="_blank" rel="noopener noreferrer">
                面向持续测试时自适应的类感知域知识融合与裂变
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        <div class="mb-2 text-base text-gray-700">
            Class-aware Domain Knowledge Fusion and Fission for Continual Test-Time Adaptation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiahuan Zhou, Chao Zhu, Zhenyu Cui, Zichen Liu, Xu Zou, Gang Hua
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注持续测试时自适应和域适应问题，属于迁移学习范畴。虽然域适应技术可能间接应用于推荐系统中的冷启动或分布偏移问题，但论文标题未明确表明与推荐、搜索或广告系统的直接关联，也未涉及LLM或Transformer架构的核心进展。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 05:09:50
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12150v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12150v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Continual Test-Time Adaptation (CTTA) aims to quickly fine-tune the model during the test phase so that it can adapt to multiple unknown downstream domain distributions without pre-acquiring downstream domain data. To this end, existing advanced CTTA methods mainly reduce the catastrophic forgetting of historical knowledge caused by irregular switching of downstream domain data by restoring the initial model or reusing historical models. However, these methods are usually accompanied by serious insufficient learning of new knowledge and interference from potentially harmful historical knowledge, resulting in severe performance degradation. To this end, we propose a class-aware domain Knowledge Fusion and Fission method for continual test-time adaptation, called KFF, which adaptively expands and merges class-aware domain knowledge in old and new domains according to the test-time data from different domains, where discriminative historical knowledge can be dynamically accumulated. Specifically, considering the huge domain gap within streaming data, a domain Knowledge FIssion (KFI) module is designed to adaptively separate new domain knowledge from a paired class-aware domain prompt pool, alleviating the impact of negative knowledge brought by old domains that are distinct from the current domain. Besides, to avoid the cumulative computation and storage overheads from continuously fissioning new knowledge, a domain Knowledge FUsion (KFU) module is further designed to merge the fissioned new knowledge into the existing knowledge pool with minimal cost, where a greedy knowledge dynamic merging strategy is designed to improve the compatibility of new and old knowledge while keeping the computational efficiency. Extensive experiments on the ImageNet-C dataset verify the effectiveness of our proposed method against other methods.
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            <a href="https://www.alphaxiv.org/abs/2510.12141v1" target="_blank" rel="noopener noreferrer">
                MAPS：基于掩码归因的策略探测——一种对齐人类与模型解释的计算框架
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            MAPS: Masked Attribution-based Probing of Strategies- A computational framework to align human and model explanations
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sabine Muzellec, Yousif Kashef Alghetaa, Simon Kornblith, Kohitij Kar
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注模型解释性和人类解释的对齐，属于模型可解释性研究范畴。虽然解释性在推荐/搜索系统中可能有辅助价值，但该工作更偏向通用NLP模型的可解释性方法，与当前关注的推荐系统核心进展、LLM技术应用或Transformer架构创新等焦点领域关联度较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 04:40:23
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12141v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12141v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">q-bio.NC</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Human core object recognition depends on the selective use of visual information, but the strategies guiding these choices are difficult to measure directly. We present MAPS (Masked Attribution-based Probing of Strategies), a behaviorally validated computational tool that tests whether explanations derived from artificial neural networks (ANNs) can also explain human vision. MAPS converts attribution maps into explanation-masked images (EMIs) and compares image-by-image human accuracies on these minimal images with limited pixel budgets with accuracies on the full stimuli. MAPS provides a principled way to evaluate and choose among competing ANN interpretability methods. In silico, EMI-based behavioral similarity between models reliably recovers the ground-truth similarity computed from their attribution maps, establishing which explanation methods best capture the model's strategy. When applied to humans and macaques, MAPS identifies ANN-explanation combinations whose explanations align most closely with biological vision, achieving the behavioral validity of Bubble masks while requiring far fewer behavioral trials. Because it needs only access to model attributions and a modest set of behavioral data on the original images, MAPS avoids exhaustive psychophysics while offering a scalable tool for adjudicating explanations and linking human behavior, neural activity, and model decisions under a common standard.
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            <a href="https://www.alphaxiv.org/abs/2510.12126v1" target="_blank" rel="noopener noreferrer">
                MetaCaptioner：基于开源套件实现通用视觉描述生成
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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            MetaCaptioner: Towards Generalist Visual Captioning with Open-source Suites
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhenxin Lei, Zhangwei Gao, Changyao Tian, Erfei Cui, Guanzhou Chen, Danni Yang, ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于视觉描述生成（Visual Captioning），属于纯粹的视觉-语言多模态任务。虽然标题提到'通用'和'开源套件'，但核心内容与推荐系统、搜索或广告中的排序任务没有直接关联。视觉描述技术可能间接应用于商品描述生成，但这属于内容生成范畴而非核心排序问题。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 04:03:25
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12126v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12126v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Generalist visual captioning goes beyond a simple appearance description task, but requires integrating a series of visual cues into a caption and handling various visual domains. In this task, current open-source models present a large performance gap with commercial ones, which limits various applications such as data synthesis. To bridge the gap, this paper proposes CapFlow, a novel multi-agent collaboration workflow. CapFlow demonstrates for the first time that, by capitalizing on open-source models, it is possible to achieve caption quality on par with GPT-4.1 in various domains with an 89.5% reduction in costs. By leveraging CapFlow as the data synthesizer, we produce high-quality visual captions from image and video domains at scale, and obtain a generalist visual captioner via fine-tuning, namely MetaCaptioner. Through extensive experiments, we show that MetaCaptioner not only achieves comparable captioning capabilities with commercial models but also reaches top-tier multimodal performance in the open-source community. We hope CapFlow and MetaCaptioner can benefit future multimodal research by providing a strong and cost-effective visual captioning solution.
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            <a href="https://www.alphaxiv.org/abs/2510.12107v1" target="_blank" rel="noopener noreferrer">
                DRL：用于类增量学习的并行适配器判别式表示学习
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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            DRL: Discriminative Representation Learning with Parallel Adapters for Class Incremental Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiawei Zhan, Jun Liu, Jinlong Peng, Xiaochen Chen, Bin-Bin Gao, Yong Liu, Chengj...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注类增量学习中的表示学习，虽然涉及表示学习技术，但其核心应用场景是计算机视觉领域的分类任务，与推荐系统、搜索或广告的关联性较弱。论文中提到的并行适配器架构可能对模型效率有一定启发，但这种增量学习技术在RecSys/Search/Ads中的直接应用潜力有限。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 03:19:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12107v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12107v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">68T05</span><span class="category-tag">68T07</span><span class="category-tag">I.2.6; I.5.4</span></div>
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                    With the excellent representation capabilities of Pre-Trained Models (PTMs), remarkable progress has been made in non-rehearsal Class-Incremental Learning (CIL) research. However, it remains an extremely challenging task due to three conundrums: increasingly large model complexity, non-smooth representation shift during incremental learning and inconsistency between stage-wise sub-problem optimization and global inference. In this work, we propose the Discriminative Representation Learning (DRL) framework to specifically address these challenges. To conduct incremental learning effectively and yet efficiently, the DRL's network, called Incremental Parallel Adapter (IPA) network, is built upon a PTM and increasingly augments the model by learning a lightweight adapter with a small amount of parameter learning overhead in each incremental stage. The adapter is responsible for adapting the model to new classes, it can inherit and propagate the representation capability from the current model through parallel connection between them by a transfer gate. As a result, this design guarantees a smooth representation shift between different incremental stages. Furthermore, to alleviate inconsistency and enable comparable feature representations across incremental stages, we design the Decoupled Anchor Supervision (DAS). It decouples constraints of positive and negative samples by respectively comparing them with the virtual anchor. This decoupling promotes discriminative representation learning and aligns the feature spaces learned at different stages, thereby narrowing the gap between stage-wise local optimization over a subset of data and global inference across all classes. Extensive experiments on six benchmarks reveal that our DRL consistently outperforms other state-of-the-art methods throughout the entire CIL period while maintaining high efficiency in both training and inference phases.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.12101v1" target="_blank" rel="noopener noreferrer">
                用于单次激光雷达全局定位的高斯语义场
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Gaussian Semantic Field for One-shot LiDAR Global Localization
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Pengyu Yin, Shenghai Yuan, Haozhi Cao, Xingyu Ji, Ruofei Bai, Siyu Chen, Lihua X...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要涉及激光雷达定位技术，属于机器人学和计算机视觉领域。虽然定位技术在某些特定搜索场景中可能有间接应用，但论文本身专注于传感器数据处理和几何定位，与推荐系统、搜索或广告的核心技术栈关联度极低。没有明确的潜在应用路径可以连接到RecSys/Search/Ads领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 03:08:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12101v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12101v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.RO</span><span class="category-tag">cs.CV</span></div>
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                    We present a one-shot LiDAR global localization algorithm featuring semantic disambiguation ability based on a lightweight tri-layered scene graph. While landmark semantic registration-based methods have shown promising performance improvements in global localization compared with geometric-only methods, landmarks can be repetitive and misleading for correspondence establishment. We propose to mitigate this problem by modeling semantic distributions with continuous functions learned from a population of Gaussian processes. Compared with discrete semantic labels, the continuous functions capture finer-grained geo-semantic information and also provide more detailed metric information for correspondence establishment. We insert this continuous function as the middle layer between the object layer and the metric-semantic layer, forming a tri-layered 3D scene graph, serving as a light-weight yet performant backend for one-shot localization. We term our global localization pipeline Outram-GSF (Gaussian semantic field) and conduct a wide range of experiments on publicly available data sets, validating the superior performance against the current state-of-the-art.
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            <a href="https://www.alphaxiv.org/abs/2510.12099v1" target="_blank" rel="noopener noreferrer">
                G4Splat：基于生成先验的几何引导高斯溅射
            </a>
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        <div class="mb-2 text-base text-gray-700">
            G4Splat: Geometry-Guided Gaussian Splatting with Generative Prior
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Junfeng Ni, Yixin Chen, Zhifei Yang, Yu Liu, Ruijie Lu, Song-Chun Zhu, Siyuan Hu...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要涉及3D场景重建和生成式建模的计算机视觉技术，属于纯粹的视觉领域研究。虽然提到了生成先验，但其核心是3D高斯溅射和几何引导方法，与推荐系统、搜索或广告的排序和建模需求没有直接关联，也没有明显的潜在应用路径。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 03:06:28
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12099v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12099v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Despite recent advances in leveraging generative prior from pre-trained diffusion models for 3D scene reconstruction, existing methods still face two critical limitations. First, due to the lack of reliable geometric supervision, they struggle to produce high-quality reconstructions even in observed regions, let alone in unobserved areas. Second, they lack effective mechanisms to mitigate multi-view inconsistencies in the generated images, leading to severe shape-appearance ambiguities and degraded scene geometry. In this paper, we identify accurate geometry as the fundamental prerequisite for effectively exploiting generative models to enhance 3D scene reconstruction. We first propose to leverage the prevalence of planar structures to derive accurate metric-scale depth maps, providing reliable supervision in both observed and unobserved regions. Furthermore, we incorporate this geometry guidance throughout the generative pipeline to improve visibility mask estimation, guide novel view selection, and enhance multi-view consistency when inpainting with video diffusion models, resulting in accurate and consistent scene completion. Extensive experiments on Replica, ScanNet++, and DeepBlending show that our method consistently outperforms existing baselines in both geometry and appearance reconstruction, particularly for unobserved regions. Moreover, our method naturally supports single-view inputs and unposed videos, with strong generalizability in both indoor and outdoor scenarios with practical real-world applicability. The project page is available at https://dali-jack.github.io/g4splat-web/.
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            <a href="https://www.alphaxiv.org/abs/2510.12095v1" target="_blank" rel="noopener noreferrer">
                IL3D：面向LLM驱动的3D场景生成的大规模室内布局数据集
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            IL3D: A Large-Scale Indoor Layout Dataset for LLM-Driven 3D Scene Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wenxu Zhou, Kaixuan Nie, Hang Du, Dong Yin, Wei Huang, Siqiang Guo, Xiaobo Zhang...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦3D场景生成和室内布局数据集，属于计算机视觉和3D生成领域。虽然提及LLM驱动，但核心是3D视觉内容生成，与推荐系统、搜索或广告的排名和建模需求缺乏直接关联。3D场景生成技术可能间接应用于商品展示或虚拟试穿，但这种应用过于边缘且非核心。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 03:02:33
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12095v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12095v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    In this study, we present IL3D, a large-scale dataset meticulously designed for large language model (LLM)-driven 3D scene generation, addressing the pressing demand for diverse, high-quality training data in indoor layout design. Comprising 27,816 indoor layouts across 18 prevalent room types and a library of 29,215 high-fidelity 3D object assets, IL3D is enriched with instance-level natural language annotations to support robust multimodal learning for vision-language tasks. We establish rigorous benchmarks to evaluate LLM-driven scene generation. Experimental results show that supervised fine-tuning (SFT) of LLMs on IL3D significantly improves generalization and surpasses the performance of SFT on other datasets. IL3D offers flexible multimodal data export capabilities, including point clouds, 3D bounding boxes, multiview images, depth maps, normal maps, and semantic masks, enabling seamless adaptation to various visual tasks. As a versatile and robust resource, IL3D significantly advances research in 3D scene generation and embodied intelligence, by providing high-fidelity scene data to support environment perception tasks of embodied agents.
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            <a href="https://www.alphaxiv.org/abs/2510.12075v1" target="_blank" rel="noopener noreferrer">
                领域自适应与生成对抗网络综述
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            <i class="fa fa-star mr-1"></i>2/10
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            A Review on Domain Adaption and Generative Adversarial Networks(GANs)
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Aashish Dhawan, Divyanshu Mudgal
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">虽然领域自适应技术在某些推荐系统场景中可能有应用价值，但本文主要聚焦于GANs的综述性内容，这属于生成式模型的通用研究范畴。根据指导原则，纯粹的生成式模型研究（如GANs）如果没有明确展示在推荐、搜索或广告中的具体应用，则被视为相关性较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 02:32:10
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12075v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12075v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The major challenge in today's computer vision scenario is the availability of good quality labeled data. In a field of study like image classification, where data is of utmost importance, we need to find more reliable methods which can overcome the scarcity of data to produce results comparable to previous benchmark results. In most cases, obtaining labeled data is very difficult because of the high cost of human labor and in some cases impossible. The purpose of this paper is to discuss Domain Adaptation and various methods to implement it. The main idea is to use a model trained on a particular dataset to predict on data from a different domain of the same kind, for example - a model trained on paintings of airplanes predicting on real images of airplanes
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            <a href="https://www.alphaxiv.org/abs/2510.12060v1" target="_blank" rel="noopener noreferrer">
                你的VAR模型实际上是一个高效且可解释的生成式分类器
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            Your VAR Model is Secretly an Efficient and Explainable Generative Classifier
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yi-Chung Chen, David I. Inouye, Jing Gao
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文讨论的是VAR（向量自回归）模型与生成式分类器的关系，这属于传统时间序列建模和生成模型的理论分析。虽然生成模型在推荐系统中有所应用，但VAR模型主要应用于经济预测和传统时间序列分析，与现代RecSys/Search/Ads中使用的Transformer、LLM等先进技术关联度较低，且论文没有明确展示在推荐、搜索或广告领域的直接应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 01:59:01
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12060v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12060v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Generative classifiers, which leverage conditional generative models for classification, have recently demonstrated desirable properties such as robustness to distribution shifts. However, recent progress in this area has been largely driven by diffusion-based models, whose substantial computational cost severely limits scalability. This exclusive focus on diffusion-based methods has also constrained our understanding of generative classifiers. In this work, we propose a novel generative classifier built on recent advances in visual autoregressive (VAR) modeling, which offers a new perspective for studying generative classifiers. To further enhance its performance, we introduce the Adaptive VAR Classifier$^+$ (A-VARC$^+$), which achieves a superior trade-off between accuracy and inference speed, thereby significantly improving practical applicability. Moreover, we show that the VAR-based method exhibits fundamentally different properties from diffusion-based methods. In particular, due to its tractable likelihood, the VAR-based classifier enables visual explainability via token-wise mutual information and demonstrates inherent resistance to catastrophic forgetting in class-incremental learning tasks.
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            <a href="https://www.alphaxiv.org/abs/2510.12781v1" target="_blank" rel="noopener noreferrer">
                主要口语语言的人工校正转录成本分析
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            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            Cost Analysis of Human-corrected Transcription for Predominately Oral Languages
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yacouba Diarra, Nouhoum Souleymane Coulibaly, Michael Leventhal
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注口语语言转录的人工校正成本分析，这属于语音处理领域的具体应用问题。该主题与推荐系统、搜索或广告的核心技术进展、LLM赋能技术或Transformer架构改进均无直接关联，也不涉及异构数据的统一建模方法。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 17:53:11
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12781v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12781v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Creating speech datasets for low-resource languages is a critical yet poorly understood challenge, particularly regarding the actual cost in human labor. This paper investigates the time and complexity required to produce high-quality annotated speech data for a subset of low-resource languages, low literacy Predominately Oral Languages, focusing on Bambara, a Manding language of Mali. Through a one-month field study involving ten transcribers with native proficiency, we analyze the correction of ASR-generated transcriptions of 53 hours of Bambara voice data. We report that it takes, on average, 30 hours of human labor to accurately transcribe one hour of speech data under laboratory conditions and 36 hours under field conditions. The study provides a baseline and practical insights for a large class of languages with comparable profiles undertaking the creation of NLP resources.
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            <a href="https://www.alphaxiv.org/abs/2510.12780v1" target="_blank" rel="noopener noreferrer">
                面向长音频隐私保护的内容匿名化
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        <div class="mb-2 text-base text-gray-700">
            Content Anonymization for Privacy in Long-form Audio
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Cristina Aggazzotti, Ashi Garg, Zexin Cai, Nicholas Andrews
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于音频隐私保护技术，属于明确的无关主题范畴。内容匿名化主要用于隐私保护目的，与推荐系统、搜索或广告的核心技术进展、LLM技术应用或Transformer架构改进没有任何关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 17:52:50
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12780v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12780v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.SD</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Voice anonymization techniques have been found to successfully obscure a speaker's acoustic identity in short, isolated utterances in benchmarks such as the VoicePrivacy Challenge. In practice, however, utterances seldom occur in isolation: long-form audio is commonplace in domains such as interviews, phone calls, and meetings. In these cases, many utterances from the same speaker are available, which pose a significantly greater privacy risk: given multiple utterances from the same speaker, an attacker could exploit an individual's vocabulary, syntax, and turns of phrase to re-identify them, even when their voice is completely disguised. To address this risk, we propose new content anonymization approaches. Our approach performs a contextual rewriting of the transcripts in an ASR-TTS pipeline to eliminate speaker-specific style while preserving meaning. We present results in a long-form telephone conversation setting demonstrating the effectiveness of a content-based attack on voice-anonymized speech. Then we show how the proposed content-based anonymization methods can mitigate this risk while preserving speech utility. Overall, we find that paraphrasing is an effective defense against content-based attacks and recommend that stakeholders adopt this step to ensure anonymity in long-form audio.
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            <a href="https://www.alphaxiv.org/abs/2510.12621v1" target="_blank" rel="noopener noreferrer">
                ACADATA：用于机器翻译的学术数据并行数据集
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            ACADATA: Parallel Dataset of Academic Data for Machine Translation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Iñaki Lacunza, Javier Garcia Gilabert, Francesca De Luca Fornaciari, Javier Aula...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于机器翻译的学术数据集构建，属于纯粹的NLP数据工程领域。论文内容与推荐系统、搜索、广告的核心技术进展或LLM/Transformer架构改进无直接关联，也没有展示在异构数据统一建模方面的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 15:20:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12621v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12621v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We present ACADATA, a high-quality parallel dataset for academic translation, that consists of two subsets: ACAD-TRAIN, which contains approximately 1.5 million author-generated paragraph pairs across 96 language directions and ACAD-BENCH, a curated evaluation set of almost 6,000 translations covering 12 directions. To validate its utility, we fine-tune two Large Language Models (LLMs) on ACAD-TRAIN and benchmark them on ACAD-BENCH against specialized machine-translation systems, general-purpose, open-weight LLMs, and several large-scale proprietary models. Experimental results demonstrate that fine-tuning on ACAD-TRAIN leads to improvements in academic translation quality by +6.1 and +12.4 d-BLEU points on average for 7B and 2B models respectively, while also improving long-context translation in a general domain by up to 24.9% when translating out of English. The fine-tuned top-performing model surpasses the best propietary and open-weight models on academic translation domain. By releasing ACAD-TRAIN, ACAD-BENCH and the fine-tuned models, we provide the community with a valuable resource to advance research in academic domain and long-context translation.
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            <a href="https://www.alphaxiv.org/abs/2510.12476v1" target="_blank" rel="noopener noreferrer">
                当个性化策略欺骗检测器时：机器生成文本检测中的特征反转陷阱
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        <div class="mb-2 text-base text-gray-700">
            When Personalization Tricks Detectors: The Feature-Inversion Trap in Machine-Generated Text Detection
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Lang Gao, Xuhui Li, Chenxi Wang, Mingzhe Li, Wei Liu, Zirui Song, Jinghui Zhang,...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注机器生成文本检测中的个性化策略欺骗问题，这属于检测和对抗性攻击领域。虽然涉及文本生成，但核心焦点是检测方法而非推荐系统、搜索或广告的直接应用。论文内容更偏向安全性和检测可靠性，属于被排除的无关主题范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 13:10:23
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12476v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12476v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language models (LLMs) have grown more powerful in language generation, producing fluent text and even imitating personal style. Yet, this ability also heightens the risk of identity impersonation. To the best of our knowledge, no prior work has examined personalized machine-generated text (MGT) detection. In this paper, we introduce \dataset, the first benchmark for evaluating detector robustness in personalized settings, built from literary and blog texts paired with their LLM-generated imitations. Our experimental results demonstrate large performance gaps across detectors in personalized settings: some state-of-the-art models suffer significant drops. We attribute this limitation to the \textit{feature-inversion trap}, where features that are discriminative in general domains become inverted and misleading when applied to personalized text. Based on this finding, we propose \method, a simple and reliable way to predict detector performance changes in personalized settings. \method identifies latent directions corresponding to inverted features and constructs probe datasets that differ primarily along these features to evaluate detector dependence. Our experiments show that \method can accurately predict both the direction and the magnitude of post-transfer changes, showing 85\% correlation with the actual performance gaps. We hope that this work will encourage further research on personalized text detection.
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            <a href="https://www.alphaxiv.org/abs/2510.12367v1" target="_blank" rel="noopener noreferrer">
                LLM-REVal：我们现在能信任LLM评审员吗？
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            LLM-REVal: Can We Trust LLM Reviewers Yet?
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Rui Li, Jia-Chen Gu, Po-Nien Kung, Heming Xia, Junfeng liu, Xiangwen Kong, Zhifa...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于LLM作为学术评审员的可信度评估，这属于纯粹的LLM评估和基准测试范畴。虽然涉及LLM技术，但论文关注的是学术评审这一特定应用场景，与推荐系统、搜索或广告领域的核心进展、技术应用或架构创新没有直接关联，也不涉及异构数据建模或Transformer架构改进。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 10:30:20
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12367v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12367v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The rapid advancement of large language models (LLMs) has inspired researchers to integrate them extensively into the academic workflow, potentially reshaping how research is practiced and reviewed. While previous studies highlight the potential of LLMs in supporting research and peer review, their dual roles in the academic workflow and the complex interplay between research and review bring new risks that remain largely underexplored. In this study, we focus on how the deep integration of LLMs into both peer-review and research processes may influence scholarly fairness, examining the potential risks of using LLMs as reviewers by simulation. This simulation incorporates a research agent, which generates papers and revises, alongside a review agent, which assesses the submissions. Based on the simulation results, we conduct human annotations and identify pronounced misalignment between LLM-based reviews and human judgments: (1) LLM reviewers systematically inflate scores for LLM-authored papers, assigning them markedly higher scores than human-authored ones; (2) LLM reviewers persistently underrate human-authored papers with critical statements (e.g., risk, fairness), even after multiple revisions. Our analysis reveals that these stem from two primary biases in LLM reviewers: a linguistic feature bias favoring LLM-generated writing styles, and an aversion toward critical statements. These results highlight the risks and equity concerns posed to human authors and academic research if LLMs are deployed in the peer review cycle without adequate caution. On the other hand, revisions guided by LLM reviews yield quality gains in both LLM-based and human evaluations, illustrating the potential of the LLMs-as-reviewers for early-stage researchers and enhancing low-quality papers.
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            <a href="https://www.alphaxiv.org/abs/2510.12355v1" target="_blank" rel="noopener noreferrer">
                基于输入归因的脑-大语言模型对齐细粒度分析
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            Fine-grained Analysis of Brain-LLM Alignment through Input Attribution
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Michela Proietti, Roberto Capobianco, Mariya Toneva
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于脑科学（Brain）与LLM的对齐分析，这属于生物医学领域的交叉研究。虽然涉及LLM技术，但其应用场景和研究目标与推荐系统、搜索或广告领域完全无关，属于明确排除的医学/生物学应用范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 10:19:01
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12355v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12355v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Understanding the alignment between large language models (LLMs) and human brain activity can reveal computational principles underlying language processing. We introduce a fine-grained input attribution method to identify the specific words most important for brain-LLM alignment, and leverage it to study a contentious research question about brain-LLM alignment: the relationship between brain alignment (BA) and next-word prediction (NWP). Our findings reveal that BA and NWP rely on largely distinct word subsets: NWP exhibits recency and primacy biases with a focus on syntax, while BA prioritizes semantic and discourse-level information with a more targeted recency effect. This work advances our understanding of how LLMs relate to human language processing and highlights differences in feature reliance between BA and NWP. Beyond this study, our attribution method can be broadly applied to explore the cognitive relevance of model predictions in diverse language processing tasks.
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            <a href="https://www.alphaxiv.org/abs/2510.12316v1" target="_blank" rel="noopener noreferrer">
                通过事实击败有害刻板印象：基于检索增强生成的反对言论生成
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            Beating Harmful Stereotypes Through Facts: RAG-based Counter-speech Generation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Greta Damo, Elena Cabrio, Serena Villata
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于内容生成和有害言论检测，属于纯粹的NLP应用领域。虽然涉及RAG技术，但其应用场景（反对言论生成）与推荐系统、搜索或广告的核心技术需求没有直接关联，且属于被明确排除的AIGC和内容生成范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 09:20:01
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12316v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12316v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Counter-speech generation is at the core of many expert activities, such as fact-checking and hate speech, to counter harmful content. Yet, existing work treats counter-speech generation as pure text generation task, mainly based on Large Language Models or NGO experts. These approaches show severe drawbacks due to the limited reliability and coherence in the generated countering text, and in scalability, respectively. To close this gap, we introduce a novel framework to model counter-speech generation as knowledge-wise text generation process. Our framework integrates advanced Retrieval-Augmented Generation (RAG) pipelines to ensure the generation of trustworthy counter-speech for 8 main target groups identified in the hate speech literature, including women, people of colour, persons with disabilities, migrants, Muslims, Jews, LGBT persons, and other. We built a knowledge base over the United Nations Digital Library, EUR-Lex and the EU Agency for Fundamental Rights, comprising a total of 32,792 texts. We use the MultiTarget-CONAN dataset to empirically assess the quality of the generated counter-speech, both through standard metrics (i.e., JudgeLM) and a human evaluation. Results show that our framework outperforms standard LLM baselines and competitive approach, on both assessments. The resulting framework and the knowledge base pave the way for studying trustworthy and sound counter-speech generation, in hate speech and beyond.
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            <a href="https://www.alphaxiv.org/abs/2510.12255v1" target="_blank" rel="noopener noreferrer">
                浅层鲁棒性，深层脆弱性：医疗大语言模型的多轮对话评估
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            Shallow Robustness, Deep Vulnerabilities: Multi-Turn Evaluation of Medical LLMs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Blazej Manczak, Eric Lin, Francisco Eiras, James O' Neill, Vaikkunth Mugunthan
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医疗领域的大语言模型评估，属于明确的无关主题范畴。论文标题明确指向医疗应用和多轮对话评估，与搜索、推荐、广告系统的技术进展或LLM在这些领域的应用完全无关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 08:04:18
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12255v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12255v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">I.2.7; I.2.6; J.3</span></div>
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                    Large language models (LLMs) are rapidly transitioning into medical clinical use, yet their reliability under realistic, multi-turn interactions remains poorly understood. Existing evaluation frameworks typically assess single-turn question answering under idealized conditions, overlooking the complexities of medical consultations where conflicting input, misleading context, and authority influence are common. We introduce MedQA-Followup, a framework for systematically evaluating multi-turn robustness in medical question answering. Our approach distinguishes between shallow robustness (resisting misleading initial context) and deep robustness (maintaining accuracy when answers are challenged across turns), while also introducing an indirect-direct axis that separates contextual framing (indirect) from explicit suggestion (direct). Using controlled interventions on the MedQA dataset, we evaluate five state-of-the-art LLMs and find that while models perform reasonably well under shallow perturbations, they exhibit severe vulnerabilities in multi-turn settings, with accuracy dropping from 91.2% to as low as 13.5% for Claude Sonnet 4. Counterintuitively, indirect, context-based interventions are often more harmful than direct suggestions, yielding larger accuracy drops across models and exposing a significant vulnerability for clinical deployment. Further compounding analyses reveal model differences, with some showing additional performance drops under repeated interventions while others partially recovering or even improving. These findings highlight multi-turn robustness as a critical but underexplored dimension for safe and reliable deployment of medical LLMs.
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            <a href="https://www.alphaxiv.org/abs/2510.12229v1" target="_blank" rel="noopener noreferrer">
                通过机制可解释性分析微调后大语言模型中的道德偏见
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        <div class="mb-2 text-base text-gray-700">
            Analysing Moral Bias in Finetuned LLMs through Mechanistic Interpretability
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Bianca Raimondi, Daniela Dalbagno, Maurizio Gabbrielli
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM中的道德偏见分析和机制可解释性，这属于公平性、伦理等非技术性主题，明确列在无关主题中。论文没有展示在推荐系统、搜索或广告中的潜在应用，与当前技术焦点完全无关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 07:31:29
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12229v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12229v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    Large language models (LLMs) have been shown to internalize human-like biases during finetuning, yet the mechanisms by which these biases manifest remain unclear. In this work, we investigated whether the well-known Knobe effect, a moral bias in intentionality judgements, emerges in finetuned LLMs and whether it can be traced back to specific components of the model. We conducted a Layer-Patching analysis across 3 open-weights LLMs and demonstrated that the bias is not only learned during finetuning but also localized in a specific set of layers. Surprisingly, we found that patching activations from the corresponding pretrained model into just a few critical layers is sufficient to eliminate the effect. Our findings offer new evidence that social biases in LLMs can be interpreted, localized, and mitigated through targeted interventions, without the need for model retraining.
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            <a href="https://www.alphaxiv.org/abs/2510.12217v1" target="_blank" rel="noopener noreferrer">
                HALF：与部署对齐的伤害感知大语言模型公平性评估
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            HALF: Harm-Aware LLM Fairness Evaluation Aligned with Deployment
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ali Mekky, Omar El Herraoui, Preslav Nakov, Yuxia Wang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于LLM公平性评估，这明确属于被排除的'公平性、伦理'等非技术性主题。论文标题强调'伤害感知'和'公平性评估'，与我的技术焦点（如推荐系统核心算法、Transformer架构效率、LLM在搜索广告中的直接应用）完全无关，且没有展示任何在推荐、搜索或广告领域的技术应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 07:13:26
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12217v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12217v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language models (LLMs) are increasingly deployed across high-impact domains, from clinical decision support and legal analysis to hiring and education, making fairness and bias evaluation before deployment critical. However, existing evaluations lack grounding in real-world scenarios and do not account for differences in harm severity, e.g., a biased decision in surgery should not be weighed the same as a stylistic bias in text summarization. To address this gap, we introduce HALF (Harm-Aware LLM Fairness), a deployment-aligned framework that assesses model bias in realistic applications and weighs the outcomes by harm severity. HALF organizes nine application domains into three tiers (Severe, Moderate, Mild) using a five-stage pipeline. Our evaluation results across eight LLMs show that (1) LLMs are not consistently fair across domains, (2) model size or performance do not guarantee fairness, and (3) reasoning models perform better in medical decision support but worse in education. We conclude that HALF exposes a clear gap between previous benchmarking success and deployment readiness.
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            <a href="https://www.alphaxiv.org/abs/2510.12210v1" target="_blank" rel="noopener noreferrer">
                DiSTAR：基于可扩展令牌自回归表示的扩散语音生成
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            DiSTAR: Diffusion over a Scalable Token Autoregressive Representation for Speech Generation
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yakun Song, Xiaobin Zhuang, Jiawei Chen, Zhikang Niu, Guanrou Yang, Chenpeng Du,...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于语音生成领域，属于纯粹的语音技术研究。虽然提到了扩散模型和自回归表示等通用技术，但论文明确限定于语音生成应用，与推荐系统、搜索或广告领域没有直接关联。语音生成属于明确的无关主题范畴，无法看出在推荐系统、搜索或广告中的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 07:03:29
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12210v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12210v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">eess.AS</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent attempts to interleave autoregressive (AR) sketchers with diffusion-based refiners over continuous speech representations have shown promise, but they remain brittle under distribution shift and offer limited levers for controllability. We introduce DISTAR, a zero-shot text-to-speech framework that operates entirely in a discrete residual vector quantization (RVQ) code space and tightly couples an AR language model with a masked diffusion model, without forced alignment or a duration predictor. Concretely, DISTAR drafts block-level RVQ tokens with an AR language model and then performs parallel masked-diffusion infilling conditioned on the draft to complete the next block, yielding long-form synthesis with blockwise parallelism while mitigating classic AR exposure bias. The discrete code space affords explicit control at inference: DISTAR produces high-quality audio under both greedy and sample-based decoding using classifier-free guidance, supports trade-offs between robustness and diversity, and enables variable bit-rate and controllable computation via RVQ layer pruning at test time. Extensive experiments and ablations demonstrate that DISTAR surpasses state-of-the-art zero-shot TTS systems in robustness, naturalness, and speaker/style consistency, while maintaining rich output diversity. Audio samples are provided on https://anonymous.4open.science/w/DiSTAR_demo.
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            <a href="https://www.alphaxiv.org/abs/2510.12200v1" target="_blank" rel="noopener noreferrer">
                HackWorld：评估计算机使用代理在利用Web应用程序漏洞方面的能力
            </a>
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            HackWorld: Evaluating Computer-Use Agents on Exploiting Web Application Vulnerabilities
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xiaoxue Ren, Penghao Jiang, Kaixin Li, Zhiyong Huang, Xiaoning Du, Jiaojiao Jian...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于网络安全和漏洞利用评估，属于安全领域的研究。这与我的核心关注点（推荐系统、搜索、广告及相关LLM技术）完全无关，且明确属于需要排除的安全相关主题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 06:52:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12200v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12200v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CR</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Web applications are prime targets for cyberattacks as gateways to critical services and sensitive data. Traditional penetration testing is costly and expertise-intensive, making it difficult to scale with the growing web ecosystem. While language model agents show promise in cybersecurity, modern web applications demand visual understanding, dynamic content handling, and multi-step interactions that only computer-use agents (CUAs) can perform. Yet, their ability to discover and exploit vulnerabilities through graphical interfaces remains largely unexplored. We present HackWorld, the first framework for systematically evaluating CUAs' capabilities to exploit web application vulnerabilities via visual interaction. Unlike sanitized benchmarks, HackWorld includes 36 real-world applications across 11 frameworks and 7 languages, featuring realistic flaws such as injection vulnerabilities, authentication bypasses, and unsafe input handling. Using a Capture-the-Flag (CTF) setup, it tests CUAs' capacity to identify and exploit these weaknesses while navigating complex web interfaces. Evaluation of state-of-the-art CUAs reveals concerning trends: exploitation rates below 12% and low cybersecurity awareness. CUAs often fail at multi-step attack planning and misuse security tools. These results expose the current limitations of CUAs in web security contexts and highlight opportunities for developing more security-aware agents capable of effective vulnerability detection and exploitation.
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            <a href="https://www.alphaxiv.org/abs/2510.12185v1" target="_blank" rel="noopener noreferrer">
                不同步：揭示音频聊天模型中的时间偏差
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        <div class="mb-2 text-base text-gray-700">
            Not in Sync: Unveiling Temporal Bias in Audio Chat Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiayu Yao, Shenghua Liu, Yiwei Wang, Rundong Cheng, Lingrui Mei, Baolong Bi, Zhe...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于音频聊天模型中的时间偏差问题，这属于语音处理领域的技术挑战。虽然提到了模型偏差，但这是针对音频模态的特定问题，与推荐系统、搜索或广告的核心技术发展没有直接关联，也不涉及LLM在推荐/搜索/广告领域的应用或Transformer架构的改进。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 06:29:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12185v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12185v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.SD</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Audio Language Models (LALMs) are increasingly applied to audio understanding and multimodal reasoning, yet their ability to locate when events occur remains underexplored. We present the first systematic study of temporal bias in LALMs, revealing a key limitation in their timestamp prediction. For example, when asked "At which second does the lecturer introduce the key formula?", models often predict timestamps that are consistently earlier or later than the ground truth. Through controlled experiments on timestamped datasets, we find that temporal bias (i) is prevalent across datasets and models, (ii) increases with audio length - even accumulating to tens of seconds in extended recordings, and (iii) varies across event types and positions. We quantify this effect with the Temporal Bias Index (TBI), measuring systematic misalignment in predicted event timings, and complement it with a visualization framework. Our findings highlight a fundamental limitation in current LALMs and call for the development of temporally robust architectures.
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            <a href="https://www.alphaxiv.org/abs/2510.12181v1" target="_blank" rel="noopener noreferrer">
                从知识到治疗：大语言模型辅助的生物医学概念表示用于药物重定位
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            From Knowledge to Treatment: Large Language Model Assisted Biomedical Concept Representation for Drug Repurposing
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chengrui Xiang, Tengfei Ma, Xiangzheng Fu, Yiping Liu, Bosheng Song, Xiangxiang ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文明确聚焦于生物医学领域的药物重定位应用，这属于明确的无关主题范畴（医学/生物学特定领域应用）。虽然涉及LLM技术，但其应用场景与推荐系统、搜索或广告领域没有任何直接或潜在的关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 06:15:36
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12181v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12181v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    Drug repurposing plays a critical role in accelerating treatment discovery, especially for complex and rare diseases. Biomedical knowledge graphs (KGs), which encode rich clinical associations, have been widely adopted to support this task. However, existing methods largely overlook common-sense biomedical concept knowledge in real-world labs, such as mechanistic priors indicating that certain drugs are fundamentally incompatible with specific treatments. To address this gap, we propose LLaDR, a Large Language Model-assisted framework for Drug Repurposing, which improves the representation of biomedical concepts within KGs. Specifically, we extract semantically enriched treatment-related textual representations of biomedical entities from large language models (LLMs) and use them to fine-tune knowledge graph embedding (KGE) models. By injecting treatment-relevant knowledge into KGE, LLaDR largely improves the representation of biomedical concepts, enhancing semantic understanding of under-studied or complex indications. Experiments based on benchmarks demonstrate that LLaDR achieves state-of-the-art performance across different scenarios, with case studies on Alzheimer's disease further confirming its robustness and effectiveness. Code is available at https://github.com/xiaomingaaa/LLaDR.
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            <a href="https://www.alphaxiv.org/abs/2510.12133v1" target="_blank" rel="noopener noreferrer">
                SafeMT：多模态语言模型的多轮对话安全性
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            SafeMT: Multi-turn Safety for Multimodal Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Han Zhu, Juntao Dai, Jiaming Ji, Haoran Li, Chengkun Cai, Pengcheng Wen, Chi-Min...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于多模态语言模型的安全性问题，这属于安全、伦理等非技术性话题，明确列在无关主题中。虽然提到了多模态，但核心关注点是安全性而非技术架构或推荐/搜索/广告应用，因此与当前关注点完全不相关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 04:24:07
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12133v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12133v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    With the widespread use of multi-modal Large Language models (MLLMs), safety issues have become a growing concern. Multi-turn dialogues, which are more common in everyday interactions, pose a greater risk than single prompts; however, existing benchmarks do not adequately consider this situation. To encourage the community to focus on the safety issues of these models in multi-turn dialogues, we introduce SafeMT, a benchmark that features dialogues of varying lengths generated from harmful queries accompanied by images. This benchmark consists of 10,000 samples in total, encompassing 17 different scenarios and four jailbreak methods. Additionally, we propose Safety Index (SI) to evaluate the general safety of MLLMs during conversations. We assess the safety of 17 models using this benchmark and discover that the risk of successful attacks on these models increases as the number of turns in harmful dialogues rises. This observation indicates that the safety mechanisms of these models are inadequate for recognizing the hazard in dialogue interactions. We propose a dialogue safety moderator capable of detecting malicious intent concealed within conversations and providing MLLMs with relevant safety policies. Experimental results from several open-source models indicate that this moderator is more effective in reducing multi-turn ASR compared to existed guard models.
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            <a href="https://www.alphaxiv.org/abs/2510.12110v1" target="_blank" rel="noopener noreferrer">
                深度关联，高度创意：一个简单而有效的大型语言模型评估指标
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        <div class="mb-2 text-base text-gray-700">
            Deep Associations, High Creativity: A Simple yet Effective Metric for Evaluating Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ziliang Qiu, Renfen Hu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于LLM评估指标，这属于纯粹的NLP中心话题，与我的关注点无关。论文标题明确表明其核心是评估方法，而非在推荐系统、搜索或广告领域的应用或技术进展。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 03:26:28
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12110v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12110v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The evaluation of LLMs' creativity represents a crucial research domain, though challenges such as data contamination and costly human assessments often impede progress. Drawing inspiration from human creativity assessment, we propose PACE, asking LLMs to generate Parallel Association Chains to Evaluate their creativity. PACE minimizes the risk of data contamination and offers a straightforward, highly efficient evaluation, as evidenced by its strong correlation with Chatbot Arena Creative Writing rankings (Spearman's $\rho = 0.739$, $p < 0.001$) across various proprietary and open-source models. A comparative analysis of associative creativity between LLMs and humans reveals that while high-performing LLMs achieve scores comparable to average human performance, professional humans consistently outperform LLMs. Furthermore, linguistic analysis reveals that both humans and LLMs exhibit a trend of decreasing concreteness in their associations, and humans demonstrating a greater diversity of associative patterns.
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            <a href="https://www.alphaxiv.org/abs/2510.12083v1" target="_blank" rel="noopener noreferrer">
                基于人工智能的行为健康安全过滤器及数据集：用于识别文本对话中的心理健康危机
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            An AI-Based Behavioral Health Safety Filter and Dataset for Identifying Mental Health Crises in Text-Based Conversations
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Benjamin W. Nelson, Celeste Wong, Matthew T. Silvestrini, Sooyoon Shin, Alanna R...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于心理健康危机检测这一特定医疗应用领域，属于明确的无关主题。虽然涉及文本分析技术，但核心应用场景（行为健康安全）与推荐系统、搜索或广告领域没有任何关联，也不涉及LLM、Transformer架构或异构数据建模等关键技术方向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 02:47:52
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12083v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12083v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language models often mishandle psychiatric emergencies, offering harmful or inappropriate advice and enabling destructive behaviors. This study evaluated the Verily behavioral health safety filter (VBHSF) on two datasets: the Verily Mental Health Crisis Dataset containing 1,800 simulated messages and the NVIDIA Aegis AI Content Safety Dataset subsetted to 794 mental health-related messages. The two datasets were clinician-labelled and we evaluated performance using the clinician labels. Additionally, we carried out comparative performance analyses against two open source, content moderation guardrails: OpenAI Omni Moderation Latest and NVIDIA NeMo Guardrails. The VBHSF demonstrated, well-balanced performance on the Verily Mental Health Crisis Dataset v1.0, achieving high sensitivity (0.990) and specificity (0.992) in detecting any mental health crises. It achieved an F1-score of 0.939, sensitivity ranged from 0.917-0.992, and specificity was >= 0.978 in identifying specific crisis categories. When evaluated against the NVIDIA Aegis AI Content Safety Dataset 2.0, VBHSF performance remained highly sensitive (0.982) and accuracy (0.921) with reduced specificity (0.859). When compared with the NVIDIA NeMo and OpenAI Omni Moderation Latest guardrails, the VBHSF demonstrated superior performance metrics across both datasets, achieving significantly higher sensitivity in all cases (all p < 0.001) and higher specificity relative to NVIDIA NeMo (p < 0.001), but not to OpenAI Omni Moderation Latest (p = 0.094). NVIDIA NeMo and OpenAI Omni Moderation Latest exhibited inconsistent performance across specific crisis types, with sensitivity for some categories falling below 0.10. Overall, the VBHSF demonstrated robust, generalizable performance that prioritizes sensitivity to minimize missed crises, a crucial feature for healthcare applications.
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            <a href="https://www.alphaxiv.org/abs/2510.12041v1" target="_blank" rel="noopener noreferrer">
                通过输入侧推理时缩放改进文本到图像生成
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            <i class="fa fa-star mr-1"></i>1/10
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            Improving Text-to-Image Generation with Input-Side Inference-Time Scaling
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ruibo Chen, Jiacheng Pan, Heng Huang, Zhenheng Yang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于文本到图像生成技术，这属于纯粹的AIGC和内容生成领域，与我的关注点无关。论文标题表明它涉及生成模型的推理时优化，但没有显示出在推荐系统、搜索或广告领域的任何潜在应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 00:51:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12041v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12041v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Recent advances in text-to-image (T2I) generation have achieved impressive results, yet existing models often struggle with simple or underspecified prompts, leading to suboptimal image-text alignment, aesthetics, and quality. We propose a prompt rewriting framework that leverages large language models (LLMs) to refine user inputs before feeding them into T2I backbones. Our approach introduces a carefully designed reward system and an iterative direct preference optimization (DPO) training pipeline, enabling the rewriter to enhance prompts without requiring supervised fine-tuning data. We evaluate our method across diverse T2I models and benchmarks. Results show that our prompt rewriter consistently improves image-text alignment, visual quality, and aesthetics, outperforming strong baselines. Furthermore, we demonstrate strong transferability by showing that a prompt rewriter trained on one T2I backbone generalizes effectively to others without needing to be retrained. We also systematically study scalability, evaluating how performance gains scale with the capacity of the large LLM used as the rewriter. These findings highlight that prompt rewriting is an effective, scalable, and practical model-agnostic strategy for improving T2I systems. We plan to release the code and trained prompt rewriters soon.
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            <a href="https://www.alphaxiv.org/abs/2510.12040v1" target="_blank" rel="noopener noreferrer">
                大型语言模型幻觉检测的不确定性量化：基础、方法与未来方向
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            Uncertainty Quantification for Hallucination Detection in Large Language Models: Foundations, Methodology, and Future Directions
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sungmin Kang, Yavuz Faruk Bakman, Duygu Nur Yaldiz, Baturalp Buyukates, Salman A...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于LLM幻觉检测和不确定性量化，属于纯粹的NLP评估基准主题，与您的关注领域无关。虽然涉及LLM技术，但论文重点在于检测和评估幻觉，而非在推荐系统、搜索或广告领域的实际应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 00:49:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12040v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12040v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    The rapid advancement of large language models (LLMs) has transformed the landscape of natural language processing, enabling breakthroughs across a wide range of areas including question answering, machine translation, and text summarization. Yet, their deployment in real-world applications has raised concerns over reliability and trustworthiness, as LLMs remain prone to hallucinations that produce plausible but factually incorrect outputs. Uncertainty quantification (UQ) has emerged as a central research direction to address this issue, offering principled measures for assessing the trustworthiness of model generations. We begin by introducing the foundations of UQ, from its formal definition to the traditional distinction between epistemic and aleatoric uncertainty, and then highlight how these concepts have been adapted to the context of LLMs. Building on this, we examine the role of UQ in hallucination detection, where quantifying uncertainty provides a mechanism for identifying unreliable generations and improving reliability. We systematically categorize a wide spectrum of existing methods along multiple dimensions and present empirical results for several representative approaches. Finally, we discuss current limitations and outline promising future research directions, providing a clearer picture of the current landscape of LLM UQ for hallucination detection.
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            <a href="https://www.alphaxiv.org/abs/2510.12036v1" target="_blank" rel="noopener noreferrer">
                关于人类标注差异与模型公平性之间相互作用的研究
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            On the Interplay between Human Label Variation and Model Fairness
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kemal Kurniawan, Meladel Mistica, Timothy Baldwin, Jey Han Lau
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文明确聚焦于模型公平性，这属于明确排除的非技术性话题范畴。虽然涉及标注差异可能看似与技术相关，但论文的核心关注点是公平性，这超出了当前关注的技术领域范围。该研究没有显示出与推荐系统、搜索或广告的核心技术进展或使能技术相关的潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 00:43:48
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12036v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12036v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The impact of human label variation (HLV) on model fairness is an unexplored topic. This paper examines the interplay by comparing training on majority-vote labels with a range of HLV methods. Our experiments show that without explicit debiasing, HLV training methods have a positive impact on fairness.
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            <a href="https://www.alphaxiv.org/abs/2510.12796v1" target="_blank" rel="noopener noreferrer">
                DriveVLA-W0：世界模型在自动驾驶中放大数据缩放定律
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            DriveVLA-W0: World Models Amplify Data Scaling Law in Autonomous Driving
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yingyan Li, Shuyao Shang, Weisong Liu, Bing Zhan, Haochen Wang, Yuqi Wang, Yunta...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于自动驾驶领域的世界模型和数据缩放定律，属于特定领域应用。虽然涉及模型架构，但其核心应用场景（自动驾驶）与搜索、推荐、广告系统无关，且没有明确的技术迁移潜力到目标领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 17:59:47
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12796v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12796v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Scaling Vision-Language-Action (VLA) models on large-scale data offers a promising path to achieving a more generalized driving intelligence. However, VLA models are limited by a ``supervision deficit'': the vast model capacity is supervised by sparse, low-dimensional actions, leaving much of their representational power underutilized. To remedy this, we propose \textbf{DriveVLA-W0}, a training paradigm that employs world modeling to predict future images. This task generates a dense, self-supervised signal that compels the model to learn the underlying dynamics of the driving environment. We showcase the paradigm's versatility by instantiating it for two dominant VLA archetypes: an autoregressive world model for VLAs that use discrete visual tokens, and a diffusion world model for those operating on continuous visual features. Building on the rich representations learned from world modeling, we introduce a lightweight action expert to address the inference latency for real-time deployment. Extensive experiments on the NAVSIM v1/v2 benchmark and a 680x larger in-house dataset demonstrate that DriveVLA-W0 significantly outperforms BEV and VLA baselines. Crucially, it amplifies the data scaling law, showing that performance gains accelerate as the training dataset size increases.
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            <a href="https://www.alphaxiv.org/abs/2510.12795v1" target="_blank" rel="noopener noreferrer">
                CuMPerLay：学习立方多参数持久性向量化
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            CuMPerLay: Learning Cubical Multiparameter Persistence Vectorizations
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Caner Korkmaz, Brighton Nuwagira, Barış Coşkunuzer, Tolga Birdal
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文涉及拓扑数据分析中的多参数持久性同调，这是一种高度专业化的数学方法，与推荐系统、搜索或广告没有明显关联。该技术专注于代数拓扑中的向量化表示，在当前焦点领域没有已知的应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 17:59:01
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12795v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12795v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span><span class="category-tag">math.AT</span><span class="category-tag">stat.ML</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We present CuMPerLay, a novel differentiable vectorization layer that enables the integration of Cubical Multiparameter Persistence (CMP) into deep learning pipelines. While CMP presents a natural and powerful way to topologically work with images, its use is hindered by the complexity of multifiltration structures as well as the vectorization of CMP. In face of these challenges, we introduce a new algorithm for vectorizing MP homologies of cubical complexes. Our CuMPerLay decomposes the CMP into a combination of individual, learnable single-parameter persistence, where the bifiltration functions are jointly learned. Thanks to the differentiability, its robust topological feature vectors can be seamlessly used within state-of-the-art architectures such as Swin Transformers. We establish theoretical guarantees for the stability of our vectorization under generalized Wasserstein metrics. Our experiments on benchmark medical imaging and computer vision datasets show the benefit CuMPerLay on classification and segmentation performance, particularly in limited-data scenarios. Overall, CuMPerLay offers a promising direction for integrating global structural information into deep networks for structured image analysis.
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            <a href="https://www.alphaxiv.org/abs/2510.12788v1" target="_blank" rel="noopener noreferrer">
                使用单张图像进行高效真实世界去模糊：AIM 2025挑战赛报告
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            Efficient Real-World Deblurring using Single Images: AIM 2025 Challenge Report
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Daniel Feijoo, Paula Garrido-Mellado, Marcos V. Conde, Jaesung Rim, Alvaro Garci...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的图像去模糊技术，属于纯粹的视觉处理领域。虽然标题提到'真实世界'应用，但该技术主要面向图像质量提升，与推荐系统、搜索或广告中的排序、用户建模、内容理解等核心问题没有直接关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 17:57:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12788v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12788v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    This paper reviews the AIM 2025 Efficient Real-World Deblurring using Single Images Challenge, which aims to advance in efficient real-blur restoration. The challenge is based on a new test set based on the well known RSBlur dataset. Pairs of blur and degraded images in this dataset are captured using a double-camera system. Participant were tasked with developing solutions to effectively deblur these type of images while fulfilling strict efficiency constraints: fewer than 5 million model parameters and a computational budget under 200 GMACs. A total of 71 participants registered, with 4 teams finally submitting valid solutions. The top-performing approach achieved a PSNR of 31.1298 dB, showcasing the potential of efficient methods in this domain. This paper provides a comprehensive overview of the challenge, compares the proposed solutions, and serves as a valuable reference for researchers in efficient real-world image deblurring.
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            <a href="https://www.alphaxiv.org/abs/2510.12785v1" target="_blank" rel="noopener noreferrer">
                MVP4D：基于多视角肖像视频扩散的可动画化4D虚拟人生成
            </a>
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            MVP4D: Multi-View Portrait Video Diffusion for Animatable 4D Avatars
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Felix Taubner, Ruihang Zhang, Mathieu Tuli, Sherwin Bahmani, David B. Lindell
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于4D虚拟人动画生成技术，属于计算机图形学和视觉内容生成领域。虽然标题提到扩散模型，但核心应用是虚拟人动画而非推荐系统、搜索或广告的排名与建模。该技术缺乏在推荐、搜索或广告中的直接应用潜力，属于纯粹的视觉内容生成范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 17:56:14
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12785v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12785v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.GR</span></div>
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                    Digital human avatars aim to simulate the dynamic appearance of humans in virtual environments, enabling immersive experiences across gaming, film, virtual reality, and more. However, the conventional process for creating and animating photorealistic human avatars is expensive and time-consuming, requiring large camera capture rigs and significant manual effort from professional 3D artists. With the advent of capable image and video generation models, recent methods enable automatic rendering of realistic animated avatars from a single casually captured reference image of a target subject. While these techniques significantly lower barriers to avatar creation and offer compelling realism, they lack constraints provided by multi-view information or an explicit 3D representation. So, image quality and realism degrade when rendered from viewpoints that deviate strongly from the reference image. Here, we build a video model that generates animatable multi-view videos of digital humans based on a single reference image and target expressions. Our model, MVP4D, is based on a state-of-the-art pre-trained video diffusion model and generates hundreds of frames simultaneously from viewpoints varying by up to 360 degrees around a target subject. We show how to distill the outputs of this model into a 4D avatar that can be rendered in real-time. Our approach significantly improves the realism, temporal consistency, and 3D consistency of generated avatars compared to previous methods.
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            <a href="https://www.alphaxiv.org/abs/2510.12777v1" target="_blank" rel="noopener noreferrer">
                假设：通过稀疏交互理解运动
            </a>
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            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            What If : Understanding Motion Through Sparse Interactions
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Stefan Andreas Baumann, Nick Stracke, Timy Phan, Björn Ommer
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于计算机视觉中的运动理解，涉及稀疏交互模式分析。这与推荐系统、搜索或广告的核心技术领域没有直接关联，也不涉及LLM、Transformer架构或异构数据建模等关键技术。即使考虑潜在应用，运动理解在RecSys/Search/Ads领域的应用场景非常有限且不明确。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 17:52:17
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12777v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12777v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Understanding the dynamics of a physical scene involves reasoning about the diverse ways it can potentially change, especially as a result of local interactions. We present the Flow Poke Transformer (FPT), a novel framework for directly predicting the distribution of local motion, conditioned on sparse interactions termed "pokes". Unlike traditional methods that typically only enable dense sampling of a single realization of scene dynamics, FPT provides an interpretable directly accessible representation of multi-modal scene motion, its dependency on physical interactions and the inherent uncertainties of scene dynamics. We also evaluate our model on several downstream tasks to enable comparisons with prior methods and highlight the flexibility of our approach. On dense face motion generation, our generic pre-trained model surpasses specialized baselines. FPT can be fine-tuned in strongly out-of-distribution tasks such as synthetic datasets to enable significant improvements over in-domain methods in articulated object motion estimation. Additionally, predicting explicit motion distributions directly enables our method to achieve competitive performance on tasks like moving part segmentation from pokes which further demonstrates the versatility of our FPT. Code and models are publicly available at https://compvis.github.io/flow-poke-transformer.
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            <a href="https://www.alphaxiv.org/abs/2510.12765v1" target="_blank" rel="noopener noreferrer">
                高效感知图像超分辨率：AIM 2025研究与基准测试
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            Efficient Perceptual Image Super Resolution: AIM 2025 Study and Benchmark
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Bruno Longarela, Marcos V. Conde, Alvaro Garcia, Radu Timofte
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的图像超分辨率技术，属于纯粹的视觉处理任务。虽然标题提到'高效'可能涉及计算优化，但论文核心是图像增强而非推荐系统、搜索或广告应用。图像超分辨率在推荐/搜索/广告中的潜在应用非常有限且间接，不符合任何当前关注领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 17:45:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12765v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12765v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    This paper presents a comprehensive study and benchmark on Efficient Perceptual Super-Resolution (EPSR). While significant progress has been made in efficient PSNR-oriented super resolution, approaches focusing on perceptual quality metrics remain relatively inefficient. Motivated by this gap, we aim to replicate or improve the perceptual results of Real-ESRGAN while meeting strict efficiency constraints: a maximum of 5M parameters and 2000 GFLOPs, calculated for an input size of 960x540 pixels. The proposed solutions were evaluated on a novel dataset consisting of 500 test images of 4K resolution, each degraded using multiple degradation types, without providing the original high-quality counterparts. This design aims to reflect realistic deployment conditions and serves as a diverse and challenging benchmark. The top-performing approach manages to outperform Real-ESRGAN across all benchmark datasets, demonstrating the potential of efficient methods in the perceptual domain. This paper establishes the modern baselines for efficient perceptual super resolution.
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            <a href="https://www.alphaxiv.org/abs/2510.12758v1" target="_blank" rel="noopener noreferrer">
                基于注意力机制的监督式深度学习在PET头部运动估计中的应用
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        <div class="mb-2 text-base text-gray-700">
            PET Head Motion Estimation Using Supervised Deep Learning with Attention
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhuotong Cai, Tianyi Zeng, Jiazhen Zhang, Eléonore V. Lieffrig, Kathryn Fontaine...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学影像（PET）中的运动估计问题，属于医学/生物医学领域，与推荐系统、搜索或广告完全无关。论文中提到的注意力机制虽然与Transformer相关，但应用于医学影像处理，没有明显的推荐系统、搜索或广告应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 17:37:12
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12758v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12758v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Head movement poses a significant challenge in brain positron emission tomography (PET) imaging, resulting in image artifacts and tracer uptake quantification inaccuracies. Effective head motion estimation and correction are crucial for precise quantitative image analysis and accurate diagnosis of neurological disorders. Hardware-based motion tracking (HMT) has limited applicability in real-world clinical practice. To overcome this limitation, we propose a deep-learning head motion correction approach with cross-attention (DL-HMC++) to predict rigid head motion from one-second 3D PET raw data. DL-HMC++ is trained in a supervised manner by leveraging existing dynamic PET scans with gold-standard motion measurements from external HMT. We evaluate DL-HMC++ on two PET scanners (HRRT and mCT) and four radiotracers (18F-FDG, 18F-FPEB, 11C-UCB-J, and 11C-LSN3172176) to demonstrate the effectiveness and generalization of the approach in large cohort PET studies. Quantitative and qualitative results demonstrate that DL-HMC++ consistently outperforms state-of-the-art data-driven motion estimation methods, producing motion-free images with clear delineation of brain structures and reduced motion artifacts that are indistinguishable from gold-standard HMT. Brain region of interest standard uptake value analysis exhibits average difference ratios between DL-HMC++ and gold-standard HMT to be 1.2 plus-minus 0.5% for HRRT and 0.5 plus-minus 0.2% for mCT. DL-HMC++ demonstrates the potential for data-driven PET head motion correction to remove the burden of HMT, making motion correction accessible to clinical populations beyond research settings. The code is available at https://github.com/maxxxxxxcai/DL-HMC-TMI.
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            <a href="https://www.alphaxiv.org/abs/2510.12753v1" target="_blank" rel="noopener noreferrer">
                E-MoFlow：通过隐式正则化从事件数据中学习自我运动和光流
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            E-MoFlow: Learning Egomotion and Optical Flow from Event Data via Implicit Regularization
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wenpu Li, Bangyan Liao, Yi Zhou, Qi Xu, Pian Wan, Peidong Liu
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于计算机视觉中的事件相机数据处理，涉及自我运动估计和光流计算，属于纯粹的视觉技术范畴。该工作与推荐系统、搜索或广告领域没有明显的直接关联，也不涉及LLM技术、Transformer架构或异构数据建模，因此完全不相关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 17:33:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12753v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12753v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    The estimation of optical flow and 6-DoF ego-motion, two fundamental tasks in 3D vision, has typically been addressed independently. For neuromorphic vision (e.g., event cameras), however, the lack of robust data association makes solving the two problems separately an ill-posed challenge, especially in the absence of supervision via ground truth. Existing works mitigate this ill-posedness by either enforcing the smoothness of the flow field via an explicit variational regularizer or leveraging explicit structure-and-motion priors in the parametrization to improve event alignment. The former notably introduces bias in results and computational overhead, while the latter, which parametrizes the optical flow in terms of the scene depth and the camera motion, often converges to suboptimal local minima. To address these issues, we propose an unsupervised framework that jointly optimizes egomotion and optical flow via implicit spatial-temporal and geometric regularization. First, by modeling camera's egomotion as a continuous spline and optical flow as an implicit neural representation, our method inherently embeds spatial-temporal coherence through inductive biases. Second, we incorporate structure-and-motion priors through differential geometric constraints, bypassing explicit depth estimation while maintaining rigorous geometric consistency. As a result, our framework (called E-MoFlow) unifies egomotion and optical flow estimation via implicit regularization under a fully unsupervised paradigm. Experiments demonstrate its versatility to general 6-DoF motion scenarios, achieving state-of-the-art performance among unsupervised methods and competitive even with supervised approaches.
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            <a href="https://www.alphaxiv.org/abs/2510.12750v1" target="_blank" rel="noopener noreferrer">
                VQArt-Bench：一个面向艺术与文化遗产的语义丰富视觉问答基准
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            VQArt-Bench: A semantically rich VQA Benchmark for Art and Cultural Heritage
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>A. Alfarano, L. Venturoli, D. Negueruela del Castillo
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于艺术与文化遗产领域的视觉问答基准测试，属于纯粹的视觉语言模型评估范畴。虽然涉及VLM技术，但应用领域（艺术文化遗产）与推荐系统、搜索或广告无关，且没有展示在相关领域应用的潜力。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 17:29:52
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12750v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12750v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Multimodal Large Language Models (MLLMs) have demonstrated significant capabilities in joint visual and linguistic tasks. However, existing Visual Question Answering (VQA) benchmarks often fail to evaluate deep semantic understanding, particularly in complex domains like visual art analysis. Confined to simple syntactic structures and surface-level attributes, these questions fail to capture the diversity and depth of human visual inquiry. This limitation incentivizes models to exploit statistical shortcuts rather than engage in visual reasoning. To address this gap, we introduce VQArt-Bench, a new, large-scale VQA benchmark for the cultural heritage domain. This benchmark is constructed using a novel multi-agent pipeline where specialized agents collaborate to generate nuanced, validated, and linguistically diverse questions. The resulting benchmark is structured along relevant visual understanding dimensions that probe a model's ability to interpret symbolic meaning, narratives, and complex visual relationships. Our evaluation of 14 state-of-the-art MLLMs on this benchmark reveals significant limitations in current models, including a surprising weakness in simple counting tasks and a clear performance gap between proprietary and open-source models.
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            <a href="https://www.alphaxiv.org/abs/2510.12749v1" target="_blank" rel="noopener noreferrer">
                SPORTS：面向城市场景理解的同步全景里程计、渲染、跟踪与分割
            </a>
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            SPORTS: Simultaneous Panoptic Odometry, Rendering, Tracking and Segmentation for Urban Scenes Understanding
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhiliu Yang, Jinyu Dai, Jianyuan Zhang, Zhu Yang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的城市场景理解，涉及里程计、渲染、跟踪和分割等纯视觉任务。这些技术主要面向自动驾驶和机器人导航领域，与推荐系统、搜索或广告的核心技术栈没有直接关联。论文内容属于纯粹的视觉技术范畴，没有展示在推荐、搜索或广告领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 17:28:19
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12749v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12749v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    The scene perception, understanding, and simulation are fundamental techniques for embodied-AI agents, while existing solutions are still prone to segmentation deficiency, dynamic objects' interference, sensor data sparsity, and view-limitation problems. This paper proposes a novel framework, named SPORTS, for holistic scene understanding via tightly integrating Video Panoptic Segmentation (VPS), Visual Odometry (VO), and Scene Rendering (SR) tasks into an iterative and unified perspective. Firstly, VPS designs an adaptive attention-based geometric fusion mechanism to align cross-frame features via enrolling the pose, depth, and optical flow modality, which automatically adjust feature maps for different decoding stages. And a post-matching strategy is integrated to improve identities tracking. In VO, panoptic segmentation results from VPS are combined with the optical flow map to improve the confidence estimation of dynamic objects, which enhances the accuracy of the camera pose estimation and completeness of the depth map generation via the learning-based paradigm. Furthermore, the point-based rendering of SR is beneficial from VO, transforming sparse point clouds into neural fields to synthesize high-fidelity RGB views and twin panoptic views. Extensive experiments on three public datasets demonstrate that our attention-based feature fusion outperforms most existing state-of-the-art methods on the odometry, tracking, segmentation, and novel view synthesis tasks.
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            <a href="https://www.alphaxiv.org/abs/2510.12747v1" target="_blank" rel="noopener noreferrer">
                FlashVSR：面向实时基于扩散模型的流式视频超分辨率
            </a>
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            <i class="fa fa-star mr-1"></i>1/10
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            FlashVSR: Towards Real-Time Diffusion-Based Streaming Video Super-Resolution
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Junhao Zhuang, Shi Guo, Xin Cai, Xiaohui Li, Yihao Liu, Chun Yuan, Tianfan Xue
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于视频超分辨率技术，属于纯粹的计算机视觉领域，与推荐系统、搜索或广告的核心技术没有直接关联。扩散模型在该论文中用于视觉质量提升，而非在推荐、搜索或广告场景中的序列建模、特征表示或用户理解等应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 17:25:54
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12747v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12747v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Diffusion models have recently advanced video restoration, but applying them to real-world video super-resolution (VSR) remains challenging due to high latency, prohibitive computation, and poor generalization to ultra-high resolutions. Our goal in this work is to make diffusion-based VSR practical by achieving efficiency, scalability, and real-time performance. To this end, we propose FlashVSR, the first diffusion-based one-step streaming framework towards real-time VSR. FlashVSR runs at approximately 17 FPS for 768x1408 videos on a single A100 GPU by combining three complementary innovations: (i) a train-friendly three-stage distillation pipeline that enables streaming super-resolution, (ii) locality-constrained sparse attention that cuts redundant computation while bridging the train-test resolution gap, and (iii) a tiny conditional decoder that accelerates reconstruction without sacrificing quality. To support large-scale training, we also construct VSR-120K, a new dataset with 120k videos and 180k images. Extensive experiments show that FlashVSR scales reliably to ultra-high resolutions and achieves state-of-the-art performance with up to 12x speedup over prior one-step diffusion VSR models. We will release the code, pretrained models, and dataset to foster future research in efficient diffusion-based VSR.
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            <a href="https://www.alphaxiv.org/abs/2510.12741v1" target="_blank" rel="noopener noreferrer">
                面向医疗保健的个性化联邦微调视觉基础模型
            </a>
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            Personalized Federated Fine-Tuning of Vision Foundation Models for Healthcare
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Adam Tupper, Christian Gagné
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及医疗领域应用和联邦学习，这两个主题均被明确列为无关主题。虽然提到了基础模型微调，但医疗领域的特定应用使其与推荐系统、搜索或广告领域完全不相关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 17:18:12
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12741v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12741v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.DC</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Foundation models open up new possibilities for the use of AI in healthcare. However, even when pre-trained on health data, they still need to be fine-tuned for specific downstream tasks. Furthermore, although foundation models reduce the amount of training data required to achieve good performance, obtaining sufficient data is still a challenge. This is due, in part, to restrictions on sharing and aggregating data from different sources to protect patients' privacy. One possible solution to this is to fine-tune foundation models via federated learning across multiple participating clients (i.e., hospitals, clinics, etc.). In this work, we propose a new personalized federated fine-tuning method that learns orthogonal LoRA adapters to disentangle general and client-specific knowledge, enabling each client to fully exploit both their own data and the data of others. Our preliminary results on real-world federated medical imaging tasks demonstrate that our approach is competitive against current federated fine-tuning methods.
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            <a href="https://www.alphaxiv.org/abs/2510.12704v1" target="_blank" rel="noopener noreferrer">
                基于Transformer的胸部X光诊断的混合解释引导学习
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            Hybrid Explanation-Guided Learning for Transformer-Based Chest X-Ray Diagnosis
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shelley Zixin Shu, Haozhe Luo, Alexander Poellinger, Mauricio Reyes
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学影像诊断（胸部X光），属于明确的医学领域应用，这在无关主题中被明确排除。虽然使用了Transformer架构，但应用场景与搜索、推荐或广告系统完全无关，且没有证据表明该方法可以迁移到这些领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 16:39:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12704v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12704v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Transformer-based deep learning models have demonstrated exceptional performance in medical imaging by leveraging attention mechanisms for feature representation and interpretability. However, these models are prone to learning spurious correlations, leading to biases and limited generalization. While human-AI attention alignment can mitigate these issues, it often depends on costly manual supervision. In this work, we propose a Hybrid Explanation-Guided Learning (H-EGL) framework that combines self-supervised and human-guided constraints to enhance attention alignment and improve generalization. The self-supervised component of H-EGL leverages class-distinctive attention without relying on restrictive priors, promoting robustness and flexibility. We validate our approach on chest X-ray classification using the Vision Transformer (ViT), where H-EGL outperforms two state-of-the-art Explanation-Guided Learning (EGL) methods, demonstrating superior classification accuracy and generalization capability. Additionally, it produces attention maps that are better aligned with human expertise.
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            <a href="https://www.alphaxiv.org/abs/2510.12679v1" target="_blank" rel="noopener noreferrer">
                MCOP：多无人机协同占用预测
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            MCOP: Multi-UAV Collaborative Occupancy Prediction
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zefu Lin, Wenbo Chen, Xiaojuan Jin, Yuran Yang, Lue Fan, Yixin Zhang, Yufeng Zha...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于无人机协同感知和占用预测，属于机器人学和自主系统领域。虽然涉及多智能体协作，但核心技术与推荐系统、搜索或广告没有直接关联，也不涉及LLM、Transformer架构或异构数据建模。该工作主要面向物理世界感知和路径规划应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 16:17:42
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12679v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12679v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Unmanned Aerial Vehicle (UAV) swarm systems necessitate efficient collaborative perception mechanisms for diverse operational scenarios. Current Bird's Eye View (BEV)-based approaches exhibit two main limitations: bounding-box representations fail to capture complete semantic and geometric information of the scene, and their performance significantly degrades when encountering undefined or occluded objects. To address these limitations, we propose a novel multi-UAV collaborative occupancy prediction framework. Our framework effectively preserves 3D spatial structures and semantics through integrating a Spatial-Aware Feature Encoder and Cross-Agent Feature Integration. To enhance efficiency, we further introduce Altitude-Aware Feature Reduction to compactly represent scene information, along with a Dual-Mask Perceptual Guidance mechanism to adaptively select features and reduce communication overhead. Due to the absence of suitable benchmark datasets, we extend three datasets for evaluation: two virtual datasets (Air-to-Pred-Occ and UAV3D-Occ) and one real-world dataset (GauUScene-Occ). Experiments results demonstrate that our method achieves state-of-the-art accuracy, significantly outperforming existing collaborative methods while reducing communication overhead to only a fraction of previous approaches.
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            <a href="https://www.alphaxiv.org/abs/2510.12670v1" target="_blank" rel="noopener noreferrer">
                TerraCodec：地球观测数据压缩
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            TerraCodec: Compressing Earth Observations
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Julen Costa-Watanabe, Isabelle Wittmann, Benedikt Blumenstiel, Konrad Schindler
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于地球观测数据的压缩技术，属于遥感或地理信息系统领域。与推荐系统、搜索、广告或LLM技术没有明显关联，也不涉及Transformer架构或多模态建模。地球观测数据压缩属于特定领域应用，不在当前关注范围内。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 16:05:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12670v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12670v1
                </a>
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Earth observation (EO) satellites produce massive streams of multispectral image time series, posing pressing challenges for storage and transmission. Yet, learned EO compression remains fragmented, lacking publicly available pretrained models and misaligned with advances in compression for natural imagery. Image codecs overlook temporal redundancy, while video codecs rely on motion priors that fail to capture the radiometric evolution of largely static scenes. We introduce TerraCodec (TEC), a family of learned codecs tailored to EO. TEC includes efficient image-based variants adapted to multispectral inputs, as well as a Temporal Transformer model (TEC-TT) that leverages dependencies across time. To overcome the fixed-rate setting of today's neural codecs, we present Latent Repacking, a novel method for training flexible-rate transformer models that operate on varying rate-distortion settings. Trained on Sentinel-2 data, TerraCodec outperforms classical codecs, achieving 3-10x stronger compression at equivalent image quality. Beyond compression, TEC-TT enables zero-shot cloud inpainting, surpassing state-of-the-art methods on the AllClear benchmark. Our results establish bespoke, learned compression algorithms as a promising direction for Earth observation. Code and model weights will be released under a permissive license.
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            <a href="https://www.alphaxiv.org/abs/2510.12660v1" target="_blank" rel="noopener noreferrer">
                关于使用分层视觉基础模型进行低成本人体网格恢复与姿态估计的研究
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            <i class="fa fa-star mr-1"></i>1/10
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            On the Use of Hierarchical Vision Foundation Models for Low-Cost Human Mesh Recovery and Pose Estimation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shuhei Tarashima, Yushan Wang, Norio Tagawa
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的人体姿态估计和网格恢复，属于纯粹的视觉技术研究。虽然标题中提到'基础模型'，但内容聚焦于视觉模态的特定应用，与推荐系统、搜索或广告的核心技术需求没有直接关联，也没有展示出在异构数据处理方面的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 15:57:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12660v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12660v1
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                    In this work, we aim to develop simple and efficient models for human mesh recovery (HMR) and its predecessor task, human pose estimation (HPE). State-of-the-art HMR methods, such as HMR2.0 and its successors, rely on large, non-hierarchical vision transformers as encoders, which are inherited from the corresponding HPE models like ViTPose. To establish baselines across varying computational budgets, we first construct three lightweight HMR2.0 variants by adapting the corresponding ViTPose models. In addition, we propose leveraging the early stages of hierarchical vision foundation models (VFMs), including Swin Transformer, GroupMixFormer, and VMamba, as encoders. This design is motivated by the observation that intermediate stages of hierarchical VFMs produce feature maps with resolutions comparable to or higher than those of non-hierarchical counterparts. We conduct a comprehensive evaluation of 27 hierarchical-VFM-based HMR and HPE models, demonstrating that using only the first two or three stages achieves performance on par with full-stage models. Moreover, we show that the resulting truncated models exhibit better trade-offs between accuracy and computational efficiency compared to existing lightweight alternatives.
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            <a href="https://www.alphaxiv.org/abs/2510.12646v1" target="_blank" rel="noopener noreferrer">
                零样本跨频一致性：基于跨频一致性的快速真实世界图像去噪
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            Zero-Shot CFC: Fast Real-World Image Denoising based on Cross-Frequency Consistency
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yanlin Jiang, Yuchen Liu, Mingren Liu
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的图像去噪技术，属于纯粹的图像处理研究方向。虽然提到了'零样本'概念，但核心内容是图像去噪算法，与推荐系统、搜索或广告的排名和建模任务没有直接关联。该技术缺乏在RecSys/Search/Ads领域的潜在应用场景。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 15:35:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12646v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12646v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Zero-shot denoisers address the dataset dependency of deep-learning-based denoisers, enabling the denoising of unseen single images. Nonetheless, existing zero-shot methods suffer from long training times and rely on the assumption of noise independence and a zero-mean property, limiting their effectiveness in real-world denoising scenarios where noise characteristics are more complicated. This paper proposes an efficient and effective method for real-world denoising, the Zero-Shot denoiser based on Cross-Frequency Consistency (ZSCFC), which enables training and denoising with a single noisy image and does not rely on assumptions about noise distribution. Specifically, image textures exhibit position similarity and content consistency across different frequency bands, while noise does not. Based on this property, we developed cross-frequency consistency loss and an ultralight network to realize image denoising. Experiments on various real-world image datasets demonstrate that our ZSCFC outperforms other state-of-the-art zero-shot methods in terms of computational efficiency and denoising performance.
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            <a href="https://www.alphaxiv.org/abs/2510.12605v1" target="_blank" rel="noopener noreferrer">
                WaterFlow：用于水下显著性掩码生成的显式物理先验校正流
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            WaterFlow: Explicit Physics-Prior Rectified Flow for Underwater Saliency Mask Generation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Runting Li, Shijie Lian, Hua Li, Yutong Li, Wenhui Wu, Sam Kwong
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的水下显著性检测，涉及物理先验和图像生成技术。这与我的关注领域（推荐系统、搜索、广告及相关的LLM/Transformer技术）完全无关，因为水下显著性掩码生成在RecSys/Search/Ads中没有任何实际应用场景。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 15:02:24
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12605v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12605v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Underwater Salient Object Detection (USOD) faces significant challenges, including underwater image quality degradation and domain gaps. Existing methods tend to ignore the physical principles of underwater imaging or simply treat degradation phenomena in underwater images as interference factors that must be eliminated, failing to fully exploit the valuable information they contain. We propose WaterFlow, a rectified flow-based framework for underwater salient object detection that innovatively incorporates underwater physical imaging information as explicit priors directly into the network training process and introduces temporal dimension modeling, significantly enhancing the model's capability for salient object identification. On the USOD10K dataset, WaterFlow achieves a 0.072 gain in S_m, demonstrating the effectiveness and superiority of our method. The code will be published after the acceptance.
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            <a href="https://www.alphaxiv.org/abs/2510.12579v1" target="_blank" rel="noopener noreferrer">
                利用Pl@ntNet智能解锁零样本植物分割
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            Unlocking Zero-Shot Plant Segmentation with Pl@ntNet Intelligence
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Simon Ravé, Jean-Christophe Lombardo, Pejman Rasti, Alexis Joly, David Rousseau
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于植物分割的计算机视觉应用，属于纯粹的视觉领域研究。虽然提到了零样本学习概念，但应用场景（植物识别）与推荐系统、搜索或广告领域没有任何关联，也不涉及LLM技术或Transformer架构的进展。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 14:38:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12579v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12579v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    We present a zero-shot segmentation approach for agricultural imagery that leverages Plantnet, a large-scale plant classification model, in conjunction with its DinoV2 backbone and the Segment Anything Model (SAM). Rather than collecting and annotating new datasets, our method exploits Plantnet's specialized plant representations to identify plant regions and produce coarse segmentation masks. These masks are then refined by SAM to yield detailed segmentations. We evaluate on four publicly available datasets of various complexity in terms of contrast including some where the limited size of the training data and complex field conditions often hinder purely supervised methods. Our results show consistent performance gains when using Plantnet-fine-tuned DinoV2 over the base DinoV2 model, as measured by the Jaccard Index (IoU). These findings highlight the potential of combining foundation models with specialized plant-centric models to alleviate the annotation bottleneck and enable effective segmentation in diverse agricultural scenarios.
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            <a href="https://www.alphaxiv.org/abs/2510.12565v1" target="_blank" rel="noopener noreferrer">
                MMOT：首个基于无人机的多光谱多目标跟踪挑战性基准
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            MMOT: The First Challenging Benchmark for Drone-based Multispectral Multi-Object Tracking
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tianhao Li, Tingfa Xu, Ying Wang, Haolin Qin, Xu Lin, Jianan Li
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于无人机视觉领域的多目标跟踪基准，属于纯粹的计算机视觉应用。虽然多目标跟踪技术在某些边缘场景可能有潜在应用，但该论文明确针对无人机和光谱分析，与推荐系统、搜索或广告的核心技术栈相距甚远，且未涉及任何Transformer架构、LLM技术或异构数据建模的相关内容。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 14:25:17
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12565v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12565v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Drone-based multi-object tracking is essential yet highly challenging due to small targets, severe occlusions, and cluttered backgrounds. Existing RGB-based tracking algorithms heavily depend on spatial appearance cues such as color and texture, which often degrade in aerial views, compromising reliability. Multispectral imagery, capturing pixel-level spectral reflectance, provides crucial cues that enhance object discriminability under degraded spatial conditions. However, the lack of dedicated multispectral UAV datasets has hindered progress in this domain. To bridge this gap, we introduce MMOT, the first challenging benchmark for drone-based multispectral multi-object tracking. It features three key characteristics: (i) Large Scale - 125 video sequences with over 488.8K annotations across eight categories; (ii) Comprehensive Challenges - covering diverse conditions such as extreme small targets, high-density scenarios, severe occlusions, and complex motion; and (iii) Precise Oriented Annotations - enabling accurate localization and reduced ambiguity under aerial perspectives. To better extract spectral features and leverage oriented annotations, we further present a multispectral and orientation-aware MOT scheme adapting existing methods, featuring: (i) a lightweight Spectral 3D-Stem integrating spectral features while preserving compatibility with RGB pretraining; (ii) an orientation-aware Kalman filter for precise state estimation; and (iii) an end-to-end orientation-adaptive transformer. Extensive experiments across representative trackers consistently show that multispectral input markedly improves tracking performance over RGB baselines, particularly for small and densely packed objects. We believe our work will advance drone-based multispectral multi-object tracking research. Our MMOT, code, and benchmarks are publicly available at https://github.com/Annzstbl/MMOT.
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            <a href="https://www.alphaxiv.org/abs/2510.12560v1" target="_blank" rel="noopener noreferrer">
                CoIRL-AD：用于自动驾驶的潜在世界模型中协作-竞争模仿-强化学习
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            CoIRL-AD: Collaborative-Competitive Imitation-Reinforcement Learning in Latent World Models for Autonomous Driving
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xiaoji Zheng, Ziyuan Yang, Yanhao Chen, Yuhang Peng, Yuanrong Tang, Gengyuan Liu...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于自动驾驶领域的强化学习和世界模型，这属于明确的无关主题。论文标题中没有任何元素表明与推荐系统、搜索或广告相关，也没有涉及LLM技术、Transformer架构或异构数据建模。该研究纯粹是自动驾驶领域的特定应用，与我的关注焦点完全无关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 14:21:52
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12560v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12560v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span><span class="category-tag">cs.RO</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    End-to-end autonomous driving models trained solely with imitation learning (IL) often suffer from poor generalization. In contrast, reinforcement learning (RL) promotes exploration through reward maximization but faces challenges such as sample inefficiency and unstable convergence. A natural solution is to combine IL and RL. Moving beyond the conventional two-stage paradigm (IL pretraining followed by RL fine-tuning), we propose CoIRL-AD, a competitive dual-policy framework that enables IL and RL agents to interact during training. CoIRL-AD introduces a competition-based mechanism that facilitates knowledge exchange while preventing gradient conflicts. Experiments on the nuScenes dataset show an 18% reduction in collision rate compared to baselines, along with stronger generalization and improved performance on long-tail scenarios. Code is available at: https://github.com/SEU-zxj/CoIRL-AD.
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            <a href="https://www.alphaxiv.org/abs/2510.12524v1" target="_blank" rel="noopener noreferrer">
                基于无定向点云的Voronoi辅助扩散计算无符号距离场
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        <div class="mb-2 text-base text-gray-700">
            Voronoi-Assisted Diffusion for Computing Unsigned Distance Fields from Unoriented Points
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiayi Kong, Chen Zong, Junkai Deng, Xuhui Chen, Fei Hou, Shiqing Xin, Junhui Hou...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机图形学中的几何处理技术，涉及Voronoi图和距离场计算，属于纯粹的3D视觉和图形学领域。这些技术没有明确的推荐系统、搜索或广告应用潜力，与当前关注的LLM技术、推荐算法或异构数据建模完全无关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 13:49:53
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12524v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12524v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Unsigned Distance Fields (UDFs) provide a flexible representation for 3D shapes with arbitrary topology, including open and closed surfaces, orientable and non-orientable geometries, and non-manifold structures. While recent neural approaches have shown promise in learning UDFs, they often suffer from numerical instability, high computational cost, and limited controllability. We present a lightweight, network-free method, Voronoi-Assisted Diffusion (VAD), for computing UDFs directly from unoriented point clouds. Our approach begins by assigning bi-directional normals to input points, guided by two Voronoi-based geometric criteria encoded in an energy function for optimal alignment. The aligned normals are then diffused to form an approximate UDF gradient field, which is subsequently integrated to recover the final UDF. Experiments demonstrate that VAD robustly handles watertight and open surfaces, as well as complex non-manifold and non-orientable geometries, while remaining computationally efficient and stable.
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            <a href="https://www.alphaxiv.org/abs/2510.12493v1" target="_blank" rel="noopener noreferrer">
                BSGS：用于相机运动去模糊的双阶段3D高斯泼溅方法
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            BSGS: Bi-stage 3D Gaussian Splatting for Camera Motion Deblurring
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>An Zhao, Piaopiao Yu, Zhe Zhu, Mingqiang Wei
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D视觉和图像去模糊技术，属于纯粹的计算机视觉领域。虽然标题提到相机运动处理，但这与推荐系统、搜索或广告的核心技术没有直接关联，也不涉及LLM、Transformer架构或异构数据建模等当前关注领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 13:26:56
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12493v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12493v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    3D Gaussian Splatting has exhibited remarkable capabilities in 3D scene reconstruction.However, reconstructing high-quality 3D scenes from motion-blurred images caused by camera motion poses a significant challenge.The performance of existing 3DGS-based deblurring methods are limited due to their inherent mechanisms, such as extreme dependence on the accuracy of camera poses and inability to effectively control erroneous Gaussian primitives densification caused by motion blur.To solve these problems, we introduce a novel framework, Bi-Stage 3D Gaussian Splatting, to accurately reconstruct 3D scenes from motion-blurred images.BSGS contains two stages. First, Camera Pose Refinement roughly optimizes camera poses to reduce motion-induced distortions. Second, with fixed rough camera poses, Global RigidTransformation further corrects motion-induced blur distortions.To alleviate multi-subframe gradient conflicts, we propose a subframe gradient aggregation strategy to optimize both stages.Furthermore, a space-time bi-stage optimization strategy is introduced to dynamically adjust primitive densification thresholds and prevent premature noisy Gaussian generation in blurred regions. Comprehensive experiments verify the effectiveness of our proposed deblurring method and show its superiority over the state of the arts.
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            <a href="https://www.alphaxiv.org/abs/2510.12483v1" target="_blank" rel="noopener noreferrer">
                用于机器人操作的快速视觉运动策略
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            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            Fast Visuomotor Policy for Robotic Manipulation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jingkai Jia, Tong Yang, Xueyao Chen, Chenhuan Liu, Wenqiang Zhang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于机器人操作和视觉运动控制，属于机器人技术领域。虽然提到了策略学习，但这是针对物理机器人操作的特定应用，与推荐系统、搜索或广告的核心技术领域没有直接关联。该研究不涉及Transformer架构、LLM技术或任何可应用于RecSys/Search/Ads的通用方法。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 13:18:45
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12483v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12483v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.RO</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We present a fast and effective policy framework for robotic manipulation, named Energy Policy, designed for high-frequency robotic tasks and resource-constrained systems. Unlike existing robotic policies, Energy Policy natively predicts multimodal actions in a single forward pass, enabling high-precision manipulation at high speed. The framework is built upon two core components. First, we adopt the energy score as the learning objective to facilitate multimodal action modeling. Second, we introduce an energy MLP to implement the proposed objective while keeping the architecture simple and efficient. We conduct comprehensive experiments in both simulated environments and real-world robotic tasks to evaluate the effectiveness of Energy Policy. The results show that Energy Policy matches or surpasses the performance of state-of-the-art manipulation methods while significantly reducing computational overhead. Notably, on the MimicGen benchmark, Energy Policy achieves superior performance with at a faster inference compared to existing approaches.
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            <a href="https://www.alphaxiv.org/abs/2510.12482v1" target="_blank" rel="noopener noreferrer">
                一种具有数据增强能力的文本-图像融合方法用于参考医学图像分割
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        <div class="mb-2 text-base text-gray-700">
            A Text-Image Fusion Method with Data Augmentation Capabilities for Referring Medical Image Segmentation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shurong Chai, Rahul Kumar JAIN, Rui Xu, Shaocong Mo, Ruibo Hou, Shiyu Teng, Jiaq...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学图像分割领域，属于明确的医学应用范畴，与RecSys、搜索或广告领域无关。虽然提到了文本-图像融合方法，但其应用场景严格限定在医疗领域，不涉及推荐系统、搜索或广告中的异构数据处理。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 13:18:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12482v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12482v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Deep learning relies heavily on data augmentation to mitigate limited data, especially in medical imaging. Recent multimodal learning integrates text and images for segmentation, known as referring or text-guided image segmentation. However, common augmentations like rotation and flipping disrupt spatial alignment between image and text, weakening performance. To address this, we propose an early fusion framework that combines text and visual features before augmentation, preserving spatial consistency. We also design a lightweight generator that projects text embeddings into visual space, bridging semantic gaps. Visualization of generated pseudo-images shows accurate region localization. Our method is evaluated on three medical imaging tasks and four segmentation frameworks, achieving state-of-the-art results. Code is publicly available on GitHub: https://github.com/11yxk/MedSeg_EarlyFusion.
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            <a href="https://www.alphaxiv.org/abs/2510.12468v1" target="_blank" rel="noopener noreferrer">
                MS-GAGA：度量选择性引导的对抗生成攻击
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            MS-GAGA: Metric-Selective Guided Adversarial Generation Attack
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Dion J. X. Ho, Gabriel Lee Jun Rong, Niharika Shrivastava, Harshavardhan Abichan...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注对抗性攻击和生成方法，属于安全/隐私领域，与我的关注点（推荐系统、搜索、广告的核心进展及LLM/Transformer技术）无关。对抗性攻击主要涉及模型鲁棒性和安全性，这些被明确列为不相关主题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 13:01:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12468v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12468v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    We present MS-GAGA (Metric-Selective Guided Adversarial Generation Attack), a two-stage framework for crafting transferable and visually imperceptible adversarial examples against deepfake detectors in black-box settings. In Stage 1, a dual-stream attack module generates adversarial candidates: MNTD-PGD applies enhanced gradient calculations optimized for small perturbation budgets, while SG-PGD focuses perturbations on visually salient regions. This complementary design expands the adversarial search space and improves transferability across unseen models. In Stage 2, a metric-aware selection module evaluates candidates based on both their success against black-box models and their structural similarity (SSIM) to the original image. By jointly optimizing transferability and imperceptibility, MS-GAGA achieves up to 27% higher misclassification rates on unseen detectors compared to state-of-the-art attacks.
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            <a href="https://www.alphaxiv.org/abs/2510.12451v1" target="_blank" rel="noopener noreferrer">
                基于函数中心视角的平坦与尖锐极小值研究
            </a>
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        <div class="mb-2 text-base text-gray-700">
            A Function Centric Perspective On Flat and Sharp Minima
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Israel Mason-Williams, Gabryel Mason-Williams, Helen Yannakoudakis
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于优化理论中的平坦与尖锐极小值问题，属于通用的机器学习优化理论研究。虽然优化方法对LLM训练有基础性作用，但论文本身没有明确指向Transformer架构、推荐系统或搜索广告的具体应用场景，与当前关注的领域技术进展关联度极低。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 12:33:14
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12451v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12451v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Flat minima are widely believed to correlate with improved generalisation in deep neural networks. However, this connection has proven more nuanced in recent studies, with both theoretical counterexamples and empirical exceptions emerging in the literature. In this paper, we revisit the role of sharpness in model performance, proposing that sharpness is better understood as a function-dependent property rather than a reliable indicator of poor generalisation. We conduct extensive empirical studies, from single-objective optimisation to modern image classification tasks, showing that sharper minima often emerge when models are regularised (e.g., via SAM, weight decay, or data augmentation), and that these sharp minima can coincide with better generalisation, calibration, robustness, and functional consistency. Across a range of models and datasets, we find that baselines without regularisation tend to converge to flatter minima yet often perform worse across all safety metrics. Our findings demonstrate that function complexity, rather than flatness alone, governs the geometry of solutions, and that sharper minima can reflect more appropriate inductive biases (especially under regularisation), calling for a function-centric reappraisal of loss landscape geometry.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.12444v1" target="_blank" rel="noopener noreferrer">
                纵向放射学报告生成综述：数据集构成、方法及性能评估
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>1/10
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            A Review of Longitudinal Radiology Report Generation: Dataset Composition, Methods, and Performance Evaluation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shaoyang Zhou, Yingshu Li, Yunyi Liu, Lingqiao Liu, Lei Wang, Luping Zhou
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学影像领域的放射学报告生成，属于医疗领域的特定应用。虽然涉及生成式技术，但内容完全围绕医学诊断和放射学，与推荐系统、搜索或广告领域没有任何直接或间接关联，也不涉及任何可能应用于这些领域的核心技术进展。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 12:26:23
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12444v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12444v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Chest Xray imaging is a widely used diagnostic tool in modern medicine, and its high utilization creates substantial workloads for radiologists. To alleviate this burden, vision language models are increasingly applied to automate Chest Xray radiology report generation (CXRRRG), aiming for clinically accurate descriptions while reducing manual effort. Conventional approaches, however, typically rely on single images, failing to capture the longitudinal context necessary for producing clinically faithful comparison statements. Recently, growing attention has been directed toward incorporating longitudinal data into CXR RRG, enabling models to leverage historical studies in ways that mirror radiologists diagnostic workflows. Nevertheless, existing surveys primarily address single image CXRRRG and offer limited guidance for longitudinal settings, leaving researchers without a systematic framework for model design. To address this gap, this survey provides the first comprehensive review of longitudinal radiology report generation (LRRG). Specifically, we examine dataset construction strategies, report generation architectures alongside longitudinally tailored designs, and evaluation protocols encompassing both longitudinal specific measures and widely used benchmarks. We further summarize LRRG methods performance, alongside analyses of different ablation studies, which collectively highlight the critical role of longitudinal information and architectural design choices in improving model performance. Finally, we summarize five major limitations of current research and outline promising directions for future development, aiming to lay a foundation for advancing this emerging field.
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            <a href="https://www.alphaxiv.org/abs/2510.12408v1" target="_blank" rel="noopener noreferrer">
                使用条件流匹配模型的低场磁共振图像质量增强
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            <i class="fa fa-star mr-1"></i>1/10
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            Low-Field Magnetic Resonance Image Quality Enhancement using a Conditional Flow Matching Model
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Huu Tien Nguyen, Ahmed Karam Eldaly
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学影像领域的磁共振图像处理，属于明确的医学应用范畴，与推荐系统、搜索或广告技术领域完全无关。论文内容涉及图像质量增强技术，但这是针对特定医疗设备的医学图像处理，没有任何潜在的应用于RecSys/Search/Ads的可能性。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 11:41:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12408v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12408v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    This paper introduces a novel framework for image quality transfer based on conditional flow matching (CFM). Unlike conventional generative models that rely on iterative sampling or adversarial objectives, CFM learns a continuous flow between a noise distribution and target data distributions through the direct regression of an optimal velocity field. We evaluate this approach in the context of low-field magnetic resonance imaging (LF-MRI), a rapidly emerging modality that offers affordable and portable scanning but suffers from inherently low signal-to-noise ratio and reduced diagnostic quality. Our framework is designed to reconstruct high-field-like MR images from their corresponding low-field inputs, thereby bridging the quality gap without requiring expensive infrastructure. Experiments demonstrate that CFM not only achieves state-of-the-art performance, but also generalizes robustly to both in-distribution and out-of-distribution data. Importantly, it does so while utilizing significantly fewer parameters than competing deep learning methods. These results underline the potential of CFM as a powerful and scalable tool for MRI reconstruction, particularly in resource-limited clinical environments.
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            <a href="https://www.alphaxiv.org/abs/2510.12362v1" target="_blank" rel="noopener noreferrer">
                CurriFlow：基于课程学习的深度融合与光流时序对齐的3D语义场景补全
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            CurriFlow: Curriculum-Guided Depth Fusion with Optical Flow-Based Temporal Alignment for 3D Semantic Scene Completion
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jinzhou Lin, Jie Zhou, Wenhao Xu, Rongtao Xu, Changwei Wang, Shunpeng Chen, Kexu...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D视觉和场景补全技术，属于纯粹的计算机视觉领域。虽然涉及深度学习和时序建模，但缺乏与推荐系统、搜索或广告领域的直接关联或潜在应用场景。论文的技术方向与用户当前关注的LLM、Transformer架构及推荐系统核心进展没有明显交集。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 10:25:26
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12362v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12362v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Semantic Scene Completion (SSC) aims to infer complete 3D geometry and semantics from monocular images, serving as a crucial capability for camera-based perception in autonomous driving. However, existing SSC methods relying on temporal stacking or depth projection often lack explicit motion reasoning and struggle with occlusions and noisy depth supervision. We propose CurriFlow, a novel semantic occupancy prediction framework that integrates optical flow-based temporal alignment with curriculum-guided depth fusion. CurriFlow employs a multi-level fusion strategy to align segmentation, visual, and depth features across frames using pre-trained optical flow, thereby improving temporal consistency and dynamic object understanding. To enhance geometric robustness, a curriculum learning mechanism progressively transitions from sparse yet accurate LiDAR depth to dense but noisy stereo depth during training, ensuring stable optimization and seamless adaptation to real-world deployment. Furthermore, semantic priors from the Segment Anything Model (SAM) provide category-agnostic supervision, strengthening voxel-level semantic learning and spatial consistency. Experiments on the SemanticKITTI benchmark demonstrate that CurriFlow achieves state-of-the-art performance with a mean IoU of 16.9, validating the effectiveness of our motion-guided and curriculum-aware design for camera-based 3D semantic scene completion.
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            <a href="https://www.alphaxiv.org/abs/2510.12282v1" target="_blank" rel="noopener noreferrer">
                PAGS：面向动态驾驶场景的优先级自适应高斯泼溅
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            PAGS: Priority-Adaptive Gaussian Splatting for Dynamic Driving Scenes
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ying A, Wenzhang Sun, Chang Zeng, Chunfeng Wang, Hao Li, Jianxun Cui
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的3D场景重建技术（高斯泼溅），应用于动态驾驶场景，这属于纯粹的视觉领域研究。虽然标题提及动态场景，但缺乏与推荐系统、搜索或广告领域的直接关联，也没有涉及Transformer架构、LLM技术或异构数据建模等核心关注点。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 08:36:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12282v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12282v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Reconstructing dynamic 3D urban scenes is crucial for autonomous driving, yet current methods face a stark trade-off between fidelity and computational cost. This inefficiency stems from their semantically agnostic design, which allocates resources uniformly, treating static backgrounds and safety-critical objects with equal importance. To address this, we introduce Priority-Adaptive Gaussian Splatting (PAGS), a framework that injects task-aware semantic priorities directly into the 3D reconstruction and rendering pipeline. PAGS introduces two core contributions: (1) Semantically-Guided Pruning and Regularization strategy, which employs a hybrid importance metric to aggressively simplify non-critical scene elements while preserving fine-grained details on objects vital for navigation. (2) Priority-Driven Rendering pipeline, which employs a priority-based depth pre-pass to aggressively cull occluded primitives and accelerate the final shading computations. Extensive experiments on the Waymo and KITTI datasets demonstrate that PAGS achieves exceptional reconstruction quality, particularly on safety-critical objects, while significantly reducing training time and boosting rendering speeds to over 350 FPS.
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            <a href="https://www.alphaxiv.org/abs/2510.12258v1" target="_blank" rel="noopener noreferrer">
                用于增强医学和细胞图像语义分割的乘法损失
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            Multiplicative Loss for Enhancing Semantic Segmentation in Medical and Cellular Images
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuto Yokoi, Kazuhiro Hotta
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学和细胞图像的语义分割，属于明确的医学领域应用，与我的关注领域完全不相关。论文标题中提到的乘法损失技术是专门针对视觉分割任务的，没有显示出在推荐系统、搜索或广告领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 08:07:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12258v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12258v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    We propose two novel loss functions, Multiplicative Loss and Confidence-Adaptive Multiplicative Loss, for semantic segmentation in medical and cellular images. Although Cross Entropy and Dice Loss are widely used, their additive combination is sensitive to hyperparameters and often performs suboptimally, especially with limited data. Medical images suffer from data scarcity due to privacy, ethics, and costly annotations, requiring robust and efficient training objectives. Our Multiplicative Loss combines Cross Entropy and Dice losses multiplicatively, dynamically modulating gradients based on prediction confidence. This reduces penalties for confident correct predictions and amplifies gradients for incorrect overconfident ones, stabilizing optimization. Building on this, Confidence-Adaptive Multiplicative Loss applies a confidence-driven exponential scaling inspired by Focal Loss, integrating predicted probabilities and Dice coefficients to emphasize difficult samples. This enhances learning under extreme data scarcity by strengthening gradients when confidence is low. Experiments on cellular and medical segmentation benchmarks show our framework consistently outperforms tuned additive and existing loss functions, offering a simple, effective, and hyperparameter-free mechanism for robust segmentation under challenging data limitations.
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                Ivan-ISTD：重新思考红外小目标检测中的跨域异方差噪声扰动
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            Ivan-ISTD: Rethinking Cross-domain Heteroscedastic Noise Perturbations in Infrared Small Target Detection
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuehui Li, Yahao Lu, Haoyuan Wu, Sen Zhang, Liang Lin, Yukai Shi
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于红外小目标检测这一特定计算机视觉任务，涉及跨域噪声扰动处理。虽然提到了跨域和噪声问题，但这属于纯粹的视觉检测领域，与推荐系统、搜索或广告的核心技术没有直接关联，也没有展示出在异构数据统一建模方面的潜在应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 07:48:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12241v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12241v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">eess.IV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    In the multimedia domain, Infrared Small Target Detection (ISTD) plays a important role in drone-based multi-modality sensing. To address the dual challenges of cross-domain shift and heteroscedastic noise perturbations in ISTD, we propose a doubly wavelet-guided Invariance learning framework(Ivan-ISTD). In the first stage, we generate training samples aligned with the target domain using Wavelet-guided Cross-domain Synthesis. This wavelet-guided alignment machine accurately separates the target background through multi-frequency wavelet filtering. In the second stage, we introduce Real-domain Noise Invariance Learning, which extracts real noise characteristics from the target domain to build a dynamic noise library. The model learns noise invariance through self-supervised loss, thereby overcoming the limitations of distribution bias in traditional artificial noise modeling. Finally, we create the Dynamic-ISTD Benchmark, a cross-domain dynamic degradation dataset that simulates the distribution shifts encountered in real-world applications. Additionally, we validate the versatility of our method using other real-world datasets. Experimental results demonstrate that our approach outperforms existing state-of-the-art methods in terms of many quantitative metrics. In particular, Ivan-ISTD demonstrates excellent robustness in cross-domain scenarios. The code for this work can be found at: https://github.com/nanjin1/Ivan-ISTD.
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            <a href="https://www.alphaxiv.org/abs/2510.12219v1" target="_blank" rel="noopener noreferrer">
                DIANet：一种基于动态图像的相位感知双流网络用于微表情识别
            </a>
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            DIANet: A Phase-Aware Dual-Stream Network for Micro-Expression Recognition via Dynamic Images
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Vu Tram Anh Khuong, Luu Tu Nguyen, Thi Bich Phuong Man, Thanh Ha Le, Thi Duyen N...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的微表情识别，属于纯粹的视觉分析任务。虽然涉及动态图像处理，但微表情识别与推荐系统、搜索或广告的核心技术没有直接关联，也不涉及LLM技术、Transformer架构或异构数据建模。该研究属于特定视觉应用领域，不在当前关注范围内。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 07:15:29
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12219v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12219v1
                </a>
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Micro-expressions are brief, involuntary facial movements that typically last less than half a second and often reveal genuine emotions. Accurately recognizing these subtle expressions is critical for applications in psychology, security, and behavioral analysis. However, micro-expression recognition (MER) remains a challenging task due to the subtle and transient nature of facial cues and the limited availability of annotated data. While dynamic image (DI) representations have been introduced to summarize temporal motion into a single frame, conventional DI-based methods often overlook the distinct characteristics of different temporal phases within a micro-expression. To address this issue, this paper proposes a novel dual-stream framework, DIANet, which leverages phase-aware dynamic images - one encoding the onset-to-apex phase and the other capturing the apex-to-offset phase. Each stream is processed by a dedicated convolutional neural network, and a cross-attention fusion module is employed to adaptively integrate features from both streams based on their contextual relevance. Extensive experiments conducted on three benchmark MER datasets (CASME-II, SAMM, and MMEW) demonstrate that the proposed method consistently outperforms conventional single-phase DI-based approaches. The results highlight the importance of modeling temporal phase information explicitly and suggest a promising direction for advancing MER.
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            <a href="https://www.alphaxiv.org/abs/2510.12182v1" target="_blank" rel="noopener noreferrer">
                BEEP3D：用于3D实例分割的框监督端到端伪掩码生成
            </a>
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        <div class="mb-2 text-base text-gray-700">
            BEEP3D: Box-Supervised End-to-End Pseudo-Mask Generation for 3D Instance Segmentation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Youngju Yoo, Seho Kim, Changick Kim
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D计算机视觉中的实例分割技术，使用边界框监督生成伪掩码。虽然技术本身具有创新性，但3D实例分割与推荐系统、搜索或广告的核心领域没有直接关联，也不涉及LLM技术、Transformer架构改进或异构数据统一建模。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 06:23:18
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12182v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12182v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    3D instance segmentation is crucial for understanding complex 3D environments, yet fully supervised methods require dense point-level annotations, resulting in substantial annotation costs and labor overhead. To mitigate this, box-level annotations have been explored as a weaker but more scalable form of supervision. However, box annotations inherently introduce ambiguity in overlapping regions, making accurate point-to-instance assignment challenging. Recent methods address this ambiguity by generating pseudo-masks through training a dedicated pseudo-labeler in an additional training stage. However, such two-stage pipelines often increase overall training time and complexity, hinder end-to-end optimization. To overcome these challenges, we propose BEEP3D-Box-supervised End-to-End Pseudo-mask generation for 3D instance segmentation. BEEP3D adopts a student-teacher framework, where the teacher model serves as a pseudo-labeler and is updated by the student model via an Exponential Moving Average. To better guide the teacher model to generate precise pseudo-masks, we introduce an instance center-based query refinement that enhances position query localization and leverages features near instance centers. Additionally, we design two novel losses-query consistency loss and masked feature consistency loss-to align semantic and geometric signals between predictions and pseudo-masks. Extensive experiments on ScanNetV2 and S3DIS datasets demonstrate that BEEP3D achieves competitive or superior performance compared to state-of-the-art weakly supervised methods while remaining computationally efficient.
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            <a href="https://www.alphaxiv.org/abs/2510.12159v1" target="_blank" rel="noopener noreferrer">
                DPL：用于一次性医学分割的空间条件扩散原型增强
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            DPL: Spatial-Conditioned Diffusion Prototype Enhancement for One-Shot Medical Segmentation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ziyuan Gao, Philippe Morel
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学图像分割领域，属于明确的无关主题（医学应用）。虽然提到了扩散模型技术，但其应用场景和核心问题与推荐系统、搜索或广告领域没有任何关联。该技术无法直接应用于RecSys/Search/Ads领域，因为其针对的是医学图像处理的特定需求。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 05:28:58
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12159v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12159v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    One-shot medical image segmentation faces fundamental challenges in prototype representation due to limited annotated data and significant anatomical variability across patients. Traditional prototype-based methods rely on deterministic averaging of support features, creating brittle representations that fail to capture intra-class diversity essential for robust generalization. This work introduces Diffusion Prototype Learning (DPL), a novel framework that reformulates prototype construction through diffusion-based feature space exploration. DPL models one-shot prototypes as learnable probability distributions, enabling controlled generation of diverse yet semantically coherent prototype variants from minimal labeled data. The framework operates through three core innovations: (1) a diffusion-based prototype enhancement module that transforms single support prototypes into diverse variant sets via forward-reverse diffusion processes, (2) a spatial-aware conditioning mechanism that leverages geometric properties derived from prototype feature statistics, and (3) a conservative fusion strategy that preserves prototype fidelity while maximizing representational diversity. DPL ensures training-inference consistency by using the same diffusion enhancement and fusion pipeline in both phases. This process generates enhanced prototypes that serve as the final representations for similarity calculations, while the diffusion process itself acts as a regularizer. Extensive experiments on abdominal MRI and CT datasets demonstrate significant improvements respectively, establishing new state-of-the-art performance in one-shot medical image segmentation.
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            <a href="https://www.alphaxiv.org/abs/2510.12132v1" target="_blank" rel="noopener noreferrer">
                FedHUG：面向远程生理测量的联邦异构无监督泛化
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            FedHUG: Federated Heterogeneous Unsupervised Generalization for Remote Physiological Measurements
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xiao Yang, Jiyao Wang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文明确涉及联邦学习（标题中的'Fed'）和医疗领域应用（远程生理测量），这两个主题均在无关主题列表中明确排除。论文标题没有显示出与推荐系统、搜索、广告或相关使能技术（如Transformer架构、LLM应用）的任何潜在关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 04:17:25
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12132v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12132v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Remote physiological measurement gained wide attention, while it requires collecting users' privacy-sensitive information, and existing contactless measurements still rely on labeled client data. This presents challenges when we want to further update real-world deployed models with numerous user data lacking labels. To resolve these challenges, we instantiate a new protocol called Federated Unsupervised Domain Generalization (FUDG) in this work. Subsequently, the \textbf{Fed}erated \textbf{H}eterogeneous \textbf{U}nsupervised \textbf{G}eneralization (\textbf{FedHUG}) framework is proposed and consists of: (1) Minimal Bias Aggregation module dynamically adjusts aggregation weights based on prior-driven bias evaluation to cope with heterogeneous non-IID features from multiple domains. (2) The Global Distribution-aware Learning Controller parameterizes the label distribution and dynamically manipulates client-specific training strategies, thereby mitigating the server-client label distribution skew and long-tail issue. The proposal shows superior performance across state-of-the-art techniques in estimation with either RGB video or mmWave radar. The code will be released.
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            <a href="https://www.alphaxiv.org/abs/2510.12123v1" target="_blank" rel="noopener noreferrer">
                面向压缩单光子3D相机的硬件感知编码函数设计
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            Hardware-aware Coding Function Design for Compressive Single-Photon 3D Cameras
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>David Parra, Felipe Gutierrez-Barragan, Trevor Seets, Andreas Velten
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D视觉硬件和压缩感知技术，属于纯粹的计算机视觉领域。虽然3D相机技术在其他领域有应用，但论文标题明确指向单光子3D相机的硬件优化，与推荐系统、搜索或广告的核心技术栈没有直接关联，也不涉及LLM、Transformer或异构数据处理等焦点领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 03:52:24
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12123v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12123v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Single-photon cameras are becoming increasingly popular in time-of-flight 3D imaging because they can time-tag individual photons with extreme resolution. However, their performance is susceptible to hardware limitations, such as system bandwidth, maximum laser power, sensor data rates, and in-sensor memory and compute resources. Compressive histograms were recently introduced as a solution to the challenge of data rates through an online in-sensor compression of photon timestamp data. Although compressive histograms work within limited in-sensor memory and computational resources, they underperform when subjected to real-world illumination hardware constraints. To address this, we present a constrained optimization approach for designing practical coding functions for compressive single-photon 3D imaging. Using gradient descent, we jointly optimize an illumination and coding matrix (i.e., the coding functions) that adheres to hardware constraints. We show through extensive simulations that our coding functions consistently outperform traditional coding designs under both bandwidth and peak power constraints. This advantage is particularly pronounced in systems constrained by peak power. Finally, we show that our approach adapts to arbitrary parameterized impulse responses by evaluating it on a real-world system with a non-ideal impulse response function.
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            <a href="https://www.alphaxiv.org/abs/2510.12119v1" target="_blank" rel="noopener noreferrer">
                ImageSentinel：保护视觉数据集免遭未经授权的检索增强图像生成
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>1/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            ImageSentinel: Protecting Visual Datasets from Unauthorized Retrieval-Augmented Image Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ziyuan Luo, Yangyi Zhao, Ka Chun Cheung, Simon See, Renjie Wan
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注视觉数据集的保护和安全问题，属于隐私和安全范畴，这在Irrelevant Topics中明确排除。虽然标题提到检索增强，但核心焦点是防止未经授权的使用，而非推荐系统、搜索或广告中的技术应用。没有明确的潜在应用场景与我的当前关注点相关。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 03:45:19
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12119v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12119v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The widespread adoption of Retrieval-Augmented Image Generation (RAIG) has raised significant concerns about the unauthorized use of private image datasets. While these systems have shown remarkable capabilities in enhancing generation quality through reference images, protecting visual datasets from unauthorized use in such systems remains a challenging problem. Traditional digital watermarking approaches face limitations in RAIG systems, as the complex feature extraction and recombination processes fail to preserve watermark signals during generation. To address these challenges, we propose ImageSentinel, a novel framework for protecting visual datasets in RAIG. Our framework synthesizes sentinel images that maintain visual consistency with the original dataset. These sentinels enable protection verification through randomly generated character sequences that serve as retrieval keys. To ensure seamless integration, we leverage vision-language models to generate the sentinel images. Experimental results demonstrate that ImageSentinel effectively detects unauthorized dataset usage while preserving generation quality for authorized applications. Code is available at https://github.com/luo-ziyuan/ImageSentinel.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.12114v1" target="_blank" rel="noopener noreferrer">
                基于自监督选择性引导扩散模型的旧照片人脸修复
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            Self-Supervised Selective-Guided Diffusion Model for Old-Photo Face Restoration
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wenjie Li, Xiangyi Wang, Heng Guo, Guangwei Gao, Zhanyu Ma
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于计算机视觉领域的人脸修复任务，属于纯粹的图像处理范畴。虽然提到了扩散模型，但其应用场景（旧照片修复）与推荐系统、搜索或广告没有直接关联，也不涉及Transformer架构改进或异构数据建模。该技术没有明显的潜在应用价值于RecSys/Search/Ads领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 03:34:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12114v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12114v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Old-photo face restoration poses significant challenges due to compounded degradations such as breakage, fading, and severe blur. Existing pre-trained diffusion-guided methods either rely on explicit degradation priors or global statistical guidance, which struggle with localized artifacts or face color. We propose Self-Supervised Selective-Guided Diffusion (SSDiff), which leverages pseudo-reference faces generated by a pre-trained diffusion model under weak guidance. These pseudo-labels exhibit structurally aligned contours and natural colors, enabling region-specific restoration via staged supervision: structural guidance applied throughout the denoising process and color refinement in later steps, aligned with the coarse-to-fine nature of diffusion. By incorporating face parsing maps and scratch masks, our method selectively restores breakage regions while avoiding identity mismatch. We further construct VintageFace, a 300-image benchmark of real old face photos with varying degradation levels. SSDiff outperforms existing GAN-based and diffusion-based methods in perceptual quality, fidelity, and regional controllability. Code link: https://github.com/PRIS-CV/SSDiff.
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            <a href="https://www.alphaxiv.org/abs/2510.12098v1" target="_blank" rel="noopener noreferrer">
                一种用于快速QR码运动去模糊的自适应边缘引导双网络框架
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            An Adaptive Edge-Guided Dual-Network Framework for Fast QR Code Motion Deblurring
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jianping Li, Dongyang Guo, Wenjie Li, Wei Zhao
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的QR码图像去模糊问题，属于纯粹的视觉处理任务。虽然标题提到'自适应'和'双网络框架'等技术元素，但核心应用场景（QR码运动去模糊）与推荐系统、搜索或广告领域没有直接关联，也不涉及LLM技术、Transformer架构或异构数据建模等关注领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 03:03:47
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12098v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12098v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Unlike general image deblurring that prioritizes perceptual quality, QR code deblurring focuses on ensuring successful decoding. QR codes are characterized by highly structured patterns with sharp edges, a robust prior for restoration. Yet existing deep learning methods rarely exploit these priors explicitly. To address this gap, we propose the Edge-Guided Attention Block (EGAB), which embeds explicit edge priors into a Transformer architecture. Based on EGAB, we develop Edge-Guided Restormer (EG-Restormer), an effective network that significantly boosts the decoding rate of severely blurred QR codes. For mildly blurred inputs, we design the Lightweight and Efficient Network (LENet) for fast deblurring. We further integrate these two networks into an Adaptive Dual-network (ADNet), which dynamically selects the suitable network based on input blur severity, making it ideal for resource-constrained mobile devices. Extensive experiments show that our EG-Restormer and ADNet achieve state-of-the-art performance with a competitive speed. Project page: https://github.com/leejianping/ADNet
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            <a href="https://www.alphaxiv.org/abs/2510.12089v1" target="_blank" rel="noopener noreferrer">
                Playmate2：基于扩散Transformer与奖励反馈的无训练多角色音频驱动动画
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        <div class="mb-2 text-base text-gray-700">
            Playmate2: Training-Free Multi-Character Audio-Driven Animation via Diffusion Transformer with Reward Feedback
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xingpei Ma, Shenneng Huang, Jiaran Cai, Yuansheng Guan, Shen Zheng, Hanfeng Zhao...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机图形学中的音频驱动动画生成，属于纯粹的视觉/图形领域应用。虽然使用了Transformer架构，但内容涉及角色动画生成，与推荐系统、搜索或广告的核心技术领域没有直接关联，也不符合任何指定的关注领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 02:50:05
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12089v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12089v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent advances in diffusion models have significantly improved audio-driven human video generation, surpassing traditional methods in both quality and controllability. However, existing approaches still face challenges in lip-sync accuracy, temporal coherence for long video generation, and multi-character animation. In this work, we propose a diffusion transformer (DiT)-based framework for generating lifelike talking videos of arbitrary length, and introduce a training-free method for multi-character audio-driven animation. First, we employ a LoRA-based training strategy combined with a position shift inference approach, which enables efficient long video generation while preserving the capabilities of the foundation model. Moreover, we combine partial parameter updates with reward feedback to enhance both lip synchronization and natural body motion. Finally, we propose a training-free approach, Mask Classifier-Free Guidance (Mask-CFG), for multi-character animation, which requires no specialized datasets or model modifications and supports audio-driven animation for three or more characters. Experimental results demonstrate that our method outperforms existing state-of-the-art approaches, achieving high-quality, temporally coherent, and multi-character audio-driven video generation in a simple, efficient, and cost-effective manner.
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            <a href="https://www.alphaxiv.org/abs/2510.12069v1" target="_blank" rel="noopener noreferrer">
                VIDMP3：通过姿态与位置先验表示运动进行视频编辑
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            VIDMP3: Video Editing by Representing Motion with Pose and Position Priors
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sandeep Mishra, Oindrila Saha, Alan C. Bovik
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于视频编辑技术，涉及姿态和位置先验的运动表示，属于纯粹的视觉领域应用。该工作与推荐系统、搜索或广告的核心技术进展、LLM赋能技术、Transformer架构改进或异构数据统一建模均无直接关联，也不符合VLM类比在推荐/搜索/广告中的应用场景。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 02:20:12
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12069v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12069v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Motion-preserved video editing is crucial for creators, particularly in scenarios that demand flexibility in both the structure and semantics of swapped objects. Despite its potential, this area remains underexplored. Existing diffusion-based editing methods excel in structure-preserving tasks, using dense guidance signals to ensure content integrity. While some recent methods attempt to address structure-variable editing, they often suffer from issues such as temporal inconsistency, subject identity drift, and the need for human intervention. To address these challenges, we introduce VidMP3, a novel approach that leverages pose and position priors to learn a generalized motion representation from source videos. Our method enables the generation of new videos that maintain the original motion while allowing for structural and semantic flexibility. Both qualitative and quantitative evaluations demonstrate the superiority of our approach over existing methods. The code will be made publicly available at https://github.com/sandeep-sm/VidMP3.
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            <a href="https://www.alphaxiv.org/abs/2510.12056v1" target="_blank" rel="noopener noreferrer">
                APGNet：用于水下伪装目标检测的自适应先验引导网络
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            APGNet: Adaptive Prior-Guided for Underwater Camouflaged Object Detection
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xinxin Huang, Han Sun, Junmin Cai, Ningzhong Liu, Huiyu Zhou
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于水下计算机视觉中的伪装目标检测，属于纯粹的视觉领域应用。虽然标题提到'先验引导'和'自适应'等技术概念，但核心应用场景（水下伪装目标检测）与推荐系统、搜索或广告领域没有任何直接关联，也不涉及Transformer架构或LLM技术。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-14 01:51:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.12056v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.12056v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Detecting camouflaged objects in underwater environments is crucial for marine ecological research and resource exploration. However, existing methods face two key challenges: underwater image degradation, including low contrast and color distortion, and the natural camouflage of marine organisms. Traditional image enhancement techniques struggle to restore critical features in degraded images, while camouflaged object detection (COD) methods developed for terrestrial scenes often fail to adapt to underwater environments due to the lack of consideration for underwater optical characteristics. To address these issues, we propose APGNet, an Adaptive Prior-Guided Network, which integrates a Siamese architecture with a novel prior-guided mechanism to enhance robustness and detection accuracy. First, we employ the Multi-Scale Retinex with Color Restoration (MSRCR) algorithm for data augmentation, generating illumination-invariant images to mitigate degradation effects. Second, we design an Extended Receptive Field (ERF) module combined with a Multi-Scale Progressive Decoder (MPD) to capture multi-scale contextual information and refine feature representations. Furthermore, we propose an adaptive prior-guided mechanism that hierarchically fuses position and boundary priors by embedding spatial attention in high-level features for coarse localization and using deformable convolution to refine contours in low-level features. Extensive experimental results on two public MAS datasets demonstrate that our proposed method APGNet outperforms 15 state-of-art methods under widely used evaluation metrics.
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                expandAllButton.addEventListener('click', function() {
                    papersContainer.classList.toggle('expanded-all');
                    this.textContent = papersContainer.classList.contains('expanded-all') ? 
                        '收起全部非精选论文' : '展开全部非精选论文';
                    
                    // 更新所有论文标题前的图标状态
                    const collapsedPapers = papersContainer.querySelectorAll('.collapsed-level-1');
                    collapsedPapers.forEach(paper => {
                        const iconElement = paper.querySelector('.expand-icon');
                        if (iconElement) {
                            iconElement.className = papersContainer.classList.contains('expanded-all') ? 
                                'expand-icon fa fa-eye' : 'expand-icon fa fa-eye-slash';
                        }
                    });
                });
                
                // 找到第一个非精选论文的位置
                const firstNormalPaper = papersContainer.querySelector('.simple-paper-card');
                if (firstNormalPaper) {
                    papersContainer.insertBefore(expandAllButton, firstNormalPaper);
                }
                
                // 添加分割线用于展开分数<=1的论文
                const divider = document.createElement('div');
                divider.className = 'papers-divider';
                
                const dividerLabel = document.createElement('div');
                dividerLabel.className = 'papers-divider-label';
                dividerLabel.textContent = '点击展开更多论文（评分较低）';
                dividerLabel.addEventListener('click', function() {
                    papersContainer.classList.toggle('expanded-level-2');
                    this.textContent = papersContainer.classList.contains('expanded-level-2') ? 
                        '点击收起低分论文' : '点击展开更多论文（评分较低）';
                });
                
                divider.appendChild(dividerLabel);
                
                // 在所有非精选论文的最后一个元素后面添加分割线
                const normalPapers = papersContainer.querySelectorAll('.simple-paper-card');
                if (normalPapers.length > 0) {
                    const lastNormalPaper = normalPapers[normalPapers.length - 1];
                    papersContainer.insertBefore(divider, lastNormalPaper.nextSibling);
                }
            }
            
            // 为每个非精选论文添加点击标题展开/折叠详情的功能
            const collapsedPapers = document.querySelectorAll('.collapsed-level-1');
            collapsedPapers.forEach(paper => {
                const titleElement = paper.querySelector('h3');
                if (titleElement) {
                    titleElement.style.cursor = 'pointer';
                    
                    // 创建展开/折叠图标元素并设置样式
                    const iconElement = document.createElement('i');
                    iconElement.className = 'expand-icon fa fa-eye-slash cursor-pointer';
                    iconElement.style.marginRight = '8px';
                    
                    // 将图标插入到标题链接之前，作为同级元素
                    const linkElement = titleElement.querySelector('a');
                    if (linkElement) {
                        // 将图标直接添加到标题元素中，位于链接之前
                        titleElement.insertBefore(iconElement, linkElement);
                        
                        // 为图标单独添加点击事件处理展开/折叠
                        iconElement.addEventListener('click', function(e) {
                            e.stopPropagation(); // 阻止事件冒泡到标题元素
                            const details = paper.querySelector('.paper-details');
                            if (details) {
                                const isExpanded = details.style.display === 'block';
                                details.style.display = isExpanded ? 'none' : 'block';
                                
                                // 更新图标状态
                                this.className = isExpanded ? 
                                    'expand-icon fa fa-eye-slash cursor-pointer' : 'expand-icon fa fa-eye cursor-pointer';
                                this.style.marginRight = '8px';
                            }
                        });
                    }
                    
                    // 为标题元素添加点击事件，也可以展开/折叠，但会检查点击目标
                    titleElement.addEventListener('click', function(e) {
                        // 仅当点击的是标题本身（非链接、非图标）时才展开/折叠
                        if (!e.target.closest('a') && !e.target.closest('.expand-icon')) {
                            const details = paper.querySelector('.paper-details');
                            if (details) {
                                const isExpanded = details.style.display === 'block';
                                details.style.display = isExpanded ? 'none' : 'block';
                                
                                // 更新图标状态
                                const iconElement = this.querySelector('.expand-icon');
                                if (iconElement) {
                                    iconElement.className = isExpanded ? 
                                        'expand-icon fa fa-eye-slash cursor-pointer' : 'expand-icon fa fa-eye cursor-pointer';
                                    iconElement.style.marginRight = '8px';
                                }
                            }
                        }
                    });
                }
            });
            
            // 实现"仅显示精选"按钮功能
            const showSelectedButton = document.getElementById('show-selected');
            if (showSelectedButton) {
                showSelectedButton.addEventListener('click', function() {
                    // 显示所有精选论文，隐藏所有普通论文
                    const selectedPapers = document.querySelectorAll('.paper-card');
                    const normalPapers = document.querySelectorAll('.simple-paper-card');
                    
                    selectedPapers.forEach(paper => {
                        paper.style.display = 'block';
                    });
                    
                    normalPapers.forEach(paper => {
                        paper.style.display = 'none';
                    });
                    
                    // 更新显示计数
                    const displayCountElement = document.getElementById('display-count');
                    if (displayCountElement) {
                        displayCountElement.textContent = `显示 ${selectedPapers.length} 篇论文 (共 ${selectedPapers.length + normalPapers.length} 篇)`;
                    }
                    
                    // 更新按钮样式
                    this.className = 'px-3 py-1 bg-primary text-white rounded text-sm hover:bg-primary/90 transition-colors';
                    document.getElementById('show-all').className = 'px-3 py-1 bg-gray-200 text-gray-700 rounded text-sm hover:bg-gray-300 transition-colors';
                    
                    // 隐藏展开/折叠按钮和分割线
                    const expandToggle = document.querySelector('.expand-toggle');
                    if (expandToggle) expandToggle.style.display = 'none';
                    
                    const papersDivider = document.querySelector('.papers-divider');
                    if (papersDivider) papersDivider.style.display = 'none';
                });
            }
            
            // 实现"全部论文"按钮功能
            const showAllButton = document.getElementById('show-all');
            if (showAllButton) {
                showAllButton.addEventListener('click', function() {
                    // 显示所有论文
                    const allPapers = document.querySelectorAll('.paper-card, .simple-paper-card');
                    allPapers.forEach(paper => {
                        paper.style.display = 'block';
                    });
                    
                    // 重置折叠状态
                    papersContainer.classList.remove('expanded-all');
                    
                    // 更新显示计数
                    const displayCountElement = document.getElementById('display-count');
                    if (displayCountElement) {
                        displayCountElement.textContent = `显示 ${allPapers.length} 篇论文 (共 ${allPapers.length} 篇)`;
                    }
                    
                    // 更新按钮样式
                    this.className = 'px-3 py-1 bg-primary text-white rounded text-sm hover:bg-primary/90 transition-colors';
                    document.getElementById('show-selected').className = 'px-3 py-1 bg-gray-200 text-gray-700 rounded text-sm hover:bg-gray-300 transition-colors';
                    
                    // 重新显示展开/折叠按钮和分割线
                    const expandToggle = document.querySelector('.expand-toggle');
                    if (expandToggle) {
                        expandToggle.style.display = 'block';
                        expandToggle.textContent = '展开全部非精选论文';
                    }
                    
                    const papersDivider = document.querySelector('.papers-divider');
                    if (papersDivider) papersDivider.style.display = 'block';
                });
            }
        });
    </script>
    <script>
    
    // 初始化日历
    document.addEventListener('DOMContentLoaded', () => {
        try {
            console.log('Attempting to initialize calendar...');
            initCalendar();
        } catch (error) {
            console.error('Error initializing calendar:', error);
        }
    });
    
    // 日历初始化函数
    function initCalendar() {
        const toggleBtn = document.getElementById('date-picker-toggle');
        const datePicker = document.getElementById('date-picker');
        const calendarGrid = document.getElementById('calendar-grid');
        const prevMonthBtn = document.getElementById('prev-month');
        const nextMonthBtn = document.getElementById('next-month');
        const currentMonthEl = document.getElementById('current-month');
        const selectedDateText = document.getElementById('selected-date-text');
        
        // 当前显示的日期（从页面获取）
        const currentDateStr = document.getElementById('current-date').textContent.trim().replace(/^\d+年|月|日/g, '');
        const currentDate = new Date(currentDateStr);
        let displayYear = currentDate.getFullYear();
        let displayMonth = currentDate.getMonth();
        
        // 有论文数据的日期列表
        const availableDates = ["20251014","20251022","20251023","20251015","20251024","20251009","20251010","20251016","20251021","20251017"];
        
        // 尝试从localStorage恢复选择状态
        const savedDate = localStorage.getItem('selectedDate');
        const savedYear = localStorage.getItem('selectedYear');
        const savedMonth = localStorage.getItem('selectedMonth');
        
        // 确保页面加载时显示当前选中的日期
        // 修复持久化问题：确保每次加载都能正确恢复选中状态
        if (savedDate) {
            selectedDateText.textContent = savedDate;
            if (savedYear) displayYear = parseInt(savedYear);
            if (savedMonth) displayMonth = parseInt(savedMonth);
        } else {
            // 首次加载时，将当前页面日期保存到localStorage
            const currentPageDate = currentDateStr.replace(/\//g, '-');
            selectedDateText.textContent = currentPageDate;
            localStorage.setItem('selectedDate', currentPageDate);
            localStorage.setItem('selectedYear', currentDate.getFullYear().toString());
            localStorage.setItem('selectedMonth', currentDate.getMonth().toString());
        }
    
        // 切换日历显示状态
        toggleBtn.addEventListener('click', (e) => {
            e.stopPropagation();
            
            // 显式控制hidden类的添加和移除
            if (datePicker.classList.contains('hidden')) {
                // 显示日历 - 确保移除hidden类
                datePicker.classList.remove('hidden');
                renderCalendar();
            } else {
                // 隐藏日历
                datePicker.classList.add('hidden');
            }
        });
        
        // 点击其他区域关闭日历
        document.addEventListener('click', () => {
            if (!datePicker.classList.contains('hidden')) {
                datePicker.classList.add('hidden');
            }
        });
        
        // 阻止日历内部点击事件冒泡
        datePicker.addEventListener('click', (e) => {
            e.stopPropagation();
        });
        
        // 上月和下月按钮
        prevMonthBtn.addEventListener('click', () => {
            displayMonth--;
            if (displayMonth < 0) {
                displayMonth = 11;
                displayYear--;
            }
            renderCalendar();
        });
        
        nextMonthBtn.addEventListener('click', () => {
            displayMonth++;
            if (displayMonth > 11) {
                displayMonth = 0;
                displayYear++;
            }
            renderCalendar();
        });
        
        /**
         * 渲染日历
         */
        function renderCalendar() {
            // 清空日历网格
            calendarGrid.innerHTML = '';
            
            // 更新当前月份显示
            const monthNames = ['1月', '2月', '3月', '4月', '5月', '6月', '7月', '8月', '9月', '10月', '11月', '12月'];
            currentMonthEl.textContent = displayYear + '年' + monthNames[displayMonth];
            
            // 计算当前月份的第一天是星期几
            const firstDay = new Date(displayYear, displayMonth, 1);
            const firstDayOfWeek = firstDay.getDay();
            
            // 计算当前月份的天数
            const daysInMonth = new Date(displayYear, displayMonth + 1, 0).getDate();
            
            // 添加上月的占位天数
            for (let i = 0; i < firstDayOfWeek; i++) {
                const emptyDay = document.createElement('div');
                emptyDay.classList.add('py-1', 'text-gray-300');
                calendarGrid.appendChild(emptyDay);
            }
            
            // 获取当前日期（用于高亮显示）
            const today = new Date();
            today.setHours(0, 0, 0, 0);
            
            // 添加当前月份的天数
            for (let day = 1; day <= daysInMonth; day++) {
                const dayElement = document.createElement('div');
                const currentDateObj = new Date(displayYear, displayMonth, day);
                const dateStr = displayYear + String(displayMonth + 1).padStart(2, '0') + String(day).padStart(2, '0');
                const displayDateStr = displayYear + '-' + String(displayMonth + 1).padStart(2, '0') + '-' + String(day).padStart(2, '0');
                
                // 设置日期元素基本样式
                dayElement.textContent = day;
                
                // 检查该日期是否有论文数据
                const hasPapers = availableDates.includes(dateStr);
                
                if (hasPapers) {
                    // 有论文数据的日期样式
                    dayElement.classList.add('py-1', 'cursor-pointer', 'hover:bg-gray-100', 'rounded', 'bg-blue-50', 'font-medium');
                    
                    // 添加点击事件，跳转到对应日期的页面
                    dayElement.addEventListener('click', () => {
                        console.log('Date clicked:', displayDateStr);
                        selectedDateText.textContent = displayDateStr;
                        
                        // 保存选择状态到localStorage
                        localStorage.setItem('selectedDate', displayDateStr);
                        localStorage.setItem('selectedYear', displayYear.toString());
                        localStorage.setItem('selectedMonth', displayMonth.toString());
                        
                        datePicker.classList.add('hidden');
                        
                        // 构造目标URL并跳转
                        const targetUrl = 'arxiv_' + dateStr + '.html';
                        window.location.href = targetUrl;
                    });
                } else {
                    // 没有论文数据的日期样式（置灰不可点击）
                    dayElement.classList.add('py-1', 'text-gray-400', 'cursor-not-allowed');
                }
                
                // 高亮显示当天日期（覆盖之前的样式）
                if (currentDateObj.getTime() === today.getTime()) {
                    dayElement.classList.remove('bg-blue-50');
                    dayElement.classList.add('bg-primary', 'text-white', 'font-bold', 'shadow');
                    if (!hasPapers) {
                        // 当天没有论文时，仍然置灰但保持背景色
                        dayElement.classList.add('opacity-70');
                    }
                }
                
                // 高亮显示当前选中的日期
                if (displayDateStr === selectedDateText.textContent) {
                    dayElement.classList.add('font-bold', 'border-2', 'border-primary', 'rounded-lg', 'shadow-md');
                }
                
                // 增强有论文数据的日期样式，使其更明显
                if (hasPapers && currentDateObj.getTime() !== today.getTime()) {
                    dayElement.classList.add('bg-blue-100', 'hover:bg-blue-200', 'transition-colors', 'duration-200');
                }
                
                calendarGrid.appendChild(dayElement);
            }
        }
    }
    </script>
    </body>

</html>